System and methods for bandwidth-efficient encoding of genomic data

ABSTRACT

A system and methods for bandwidth-efficient encoding of genome and bioinformatic sequence datasets comprising a sequence analyzer configured to: analyze a received sequence dataset to determine a sequence dataset file type, scan the sequence dataset to maintain a count of unique characters contained therein, identify positions where the unique character count increases by a power of two, deconstruct the sequence dataset into a plurality of sourceblocks at the identified positions, and encode the plurality of sourceblocks using a data deconstruction engine and library management module to assign each sourceblock a reference code.

CROSS-REFERENCE TO RELATED APPLICATIONS

Priority is claimed in the application data sheet to the followingpatents or patent applications, the entire written description of eachof which is expressly incorporated herein by reference in its entirety:

Ser. No. 16/923,039

63/027,166

62/926,723

Ser. No. 16/716,098

Ser. No. 16/455,655

Ser. No. 16/200,466

Ser. No. 15/975,741

62/578,824

BACKGROUND OF THE INVENTION Field of the Invention

The present invention is in the field of computer data storage andtransmission, and in particular to the use of block cipher encryptionusing novel systems and techniques.

Discussion of the State of the Art

As computers become an ever-greater part of our lives, and especially inthe past few years, data storage has become a limiting factor worldwide.Prior to about 2010, the growth of data storage far exceeded the growthin storage demand. In fact, it was commonly considered at that time thatstorage was not an issue, and perhaps never would be, again. In 2010,however, with the growth of social media, cloud data centers, high techand biotech industries, global digital data storage acceleratedexponentially, and demand hit the zettabyte (1 trillion gigabytes)level. Current estimates are that data storage demand will reach 50zettabytes by 2020. By contrast, digital storage device manufacturersproduced roughly 1 zettabyte of physical storage capacity globally in2016. We are producing data at a much faster rate than we are producingthe capacity to store it. In short, we are running out of room to storedata, and need a breakthrough in data storage technology to keep up withdemand.

The primary solutions available at the moment are the addition ofadditional physical storage capacity and data compression. As notedabove, the addition of physical storage will not solve the problem, asstorage demand has already outstripped global manufacturing capacity.Data compression is also not a solution. A rough average compressionratio for mixed data types is 2:1, representing a doubling of storagecapacity. However, as the mix of global data storage trends towardmulti-media data (audio, video, and images), the space savings yieldedby compression either decreases substantially, as is the case withlossless compression which allows for retention of all original data inthe set, or results in degradation of data, as is the case with lossycompression which selectively discards data in order to increasecompression. Even assuming a doubling of storage capacity, datacompression cannot solve the global data storage problem. The methoddisclosed herein, on the other hand, works the same way with any type ofdata.

Transmission bandwidth is also increasingly becoming a bottleneck. Largedata sets require tremendous bandwidth, and we are transmitting more andmore data every year between large data centers. On the small end of thescale, we are adding billions of low bandwidth devices to the globalnetwork, and data transmission limitations impose constraints on thedevelopment of networked computing applications, such as the “Internetof Things”.

Furthermore, as quantum computing becomes more and more imminent, thesecurity of data, both stored data and data streaming from one point toanother via networks, becomes a critical concern as existing encryptiontechnologies are placed at risk.

Genomic data science is a field of study that enables researchers to usepowerful computational and statistical methods to decode the functionalinformation hidden in DNA sequences. Current estimates predict thatgenomics research will generate between 2 and 40 exabytes (i.e., 10¹⁸bytes) of data within the next decade. Data about a single human genomesequence alone would take up 200 gigabytes. Researchers are working toextract valuable information from such complicated and large datasets sothey can better understand human health and disease. The scientificcommunity's current ability to sequence DNA has far outpaced its abilityto decrypt the information it contains, so genomic data science will bea vibrant field of research for many years to come. Furthermore,performing genomic data science carries with it a set of ethicalresponsibilities, as each person's sequence data are associated withissues related to privacy and identity. There exists a need for a systemand method for bandwidth efficient transmission and storage of compactedgenomic data sets.

What is needed is an efficient and secure approach to encryption,storage, and transmission of genomic and bioinformatic datasets.

SUMMARY OF THE INVENTION

The inventor has developed, and reduced to practice, a system andmethods for bandwidth-efficient encoding of genome and bioinformaticsequence datasets comprising a sequence analyzer configured to: analyzea received sequence dataset to determine a sequence dataset file type,scan the sequence dataset to maintain a count of unique characterscontained therein, identify positions where the unique character countincreases by a power of two, deconstruct the sequence dataset into aplurality of sourceblocks at the identified positions, and encode theplurality of sourceblocks using a data deconstruction engine and librarymanagement module to assign each sourceblock a reference code.

According to one aspect, a system for bandwidth-efficient encoding ofgenomic data, comprising: a computing device comprising a processor, amemory, and a first plurality of programming instructions; a codebookstored in the memory of the computing device, the codebook comprising aplurality of sourceblocks and for each sourceblock a reference code; asequence analyzer comprising a second plurality of programminginstructions stored in the memory and operable on the processor, whereinthe second plurality of programming instructions, when operating on theprocessor, cause the processor to: receive a sequence dataset; scan thesequence data and maintain a count of the number of unique characterscontained within the sequence data; indicate positions in the sequencewhere the number of unique characters increase by a power of two;calculate a compaction that would be obtained by dividing the sequencedata into one of the plurality of segments at one of the indicatedpositions that yield the best compaction; deconstruct the sequencedataset into a plurality of sourceblocks at the positions that yield thebest compaction; and pass the sourceblocks to a data deconstructionengine; and a data deconstruction engine comprising a third plurality ofprogramming instructions stored in the memory and operable on theprocessor, wherein the third plurality of programming instructions, whenoperating on the processor, cause the processor to: receive thesourceblocks from the sequence analyzer; pass the sourceblocks to alibrary management module for comparison with sourceblocks alreadycontained in the codebook; receive reference codes to the sourceblocksfrom the library management module and discard the sourceblock; andcreate a plurality of codewords for storage or transmission of thesequence data; and a library management module comprising a fourthplurality of programming instructions stored in the memory and operableon the processor, wherein the fourth plurality of programminginstructions, when operating on the processor, cause the processor to:receive sourceblocks from the data deconstruction engine; return areference code to the data deconstruction engine, when the sourceblockreceived matches an existing sourceblock in the codebook; and optimallycreate new, unique reference codes to the sourceblock received, storeboth the sourceblock and the associated reference code in the codebook,and return the new reference code to the data deconstruction engine,when that sourceblock does not match an existing sourceblock in thecodebook.

In another aspect, a method for bandwidth-efficient encoding of genomicdata, comprising the steps of: storing, in a memory of a computingdevice, a codebook, the codebook comprising a plurality of sourceblocksand for each sourceblock a reference code; receiving a sequence dataset;scanning the sequence data and maintaining a count of the number ofunique characters contained within the sequence data; indicatingpositions in the sequence where the number of unique characters increaseby a power of two; calculating a compaction that would be obtained bydividing the sequence data into one of the plurality of segments at oneof the indicated positions that yield the best compaction;deconstructing the sequence dataset into a plurality of sourceblocks atthe positions that yield the best compaction; passing the sourceblocksto a data deconstruction engine; receiving the sourceblocks from thesequence analyzer; passing the sourceblocks to a library managementmodule for comparison with sourceblocks already contained in thecodebook; receiving reference codes to the sourceblocks from the librarymanagement module and discard the sourceblock; creating a plurality ofcodewords for storage or transmission of the sequence data; receivingsourceblocks from the data deconstruction engine; returning a referencecode to the data deconstruction engine, when the sourceblock receivedmatches an existing sourceblock in the codebook; and optimally creatingnew, unique reference codes to the sourceblock received, storing boththe sourceblock and the associated reference code in the codebook, andreturning the new reference code to the data deconstruction engine, whenthat sourceblock does not match an existing sourceblock in the codebook.

According to another aspect, each of the plurality of codewords containsat least a reference code to a sourceblock in the codebook, and maycontain additional information about the location of the reference codewithin the sequence dataset.

According to another aspect, the sequence dataset that is encodedcomprises a graph representing at least a portion of a plurality ofgenomes

According to another aspect, the graph is a De Bruijin graph, a directedgraph, a bi-edged graph, or a bidirected graph.

According to another aspect, the system further comprises a datareconstruction engine comprising a fifth plurality of programminginstructions stored in the memory and operable on the processor, whereinthe fifth plurality of programming instructions, when operating on theprocessor, cause the processor to: receive requests for reconstructedsequence data; retrieve the codewords associated with the requested datafrom the codebook; pass the reference codes contained in the codewordsto the library management module for retrieval of the sourceblockcontained in the codebook associated with the reference codes; assemblethe sourceblock in the proper order based on the location informationcontained in the codewords; and send out the reconstructed sequence datato the requestor.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

The accompanying drawings illustrate several aspects and, together withthe description, serve to explain the principles of the inventionaccording to the aspects. It will be appreciated by one skilled in theart that the particular arrangements illustrated in the drawings aremerely exemplary, and are not to be considered as limiting of the scopeof the invention or the claims herein in any way.

FIG. 1 is a diagram showing an embodiment of the system in which allcomponents of the system are operated locally.

FIG. 2 is a diagram showing an embodiment of one aspect of the system,the data deconstruction engine.

FIG. 3 is a diagram showing an embodiment of one aspect of the system,the data reconstruction engine.

FIG. 4 is a diagram showing an embodiment of one aspect of the system,the library management module.

FIG. 5 is a diagram showing another embodiment of the system in whichdata is transferred between remote locations.

FIG. 6 is a diagram showing an embodiment in which a standardizedversion of the sourceblock library and associated algorithms would beencoded as firmware on a dedicated processing chip included as part ofthe hardware of a plurality of devices.

FIG. 7 is a diagram showing an example of how data might be convertedinto reference codes using an aspect of an embodiment.

FIG. 8 is a method diagram showing the steps involved in using anembodiment to store data.

FIG. 9 is a method diagram showing the steps involved in using anembodiment to retrieve data.

FIG. 10 is a method diagram showing the steps involved in using anembodiment to encode data.

FIG. 11 is a method diagram showing the steps involved in using anembodiment to decode data.

FIG. 12 is a diagram showing an exemplary system architecture, accordingto a preferred embodiment of the invention.

FIG. 13 is a diagram showing a more detailed architecture for acustomized library generator.

FIG. 14 is a diagram showing a more detailed architecture for a libraryoptimizer.

FIG. 15 is a diagram showing a more detailed architecture for atransmission and storage engine.

FIG. 16 is a method diagram illustrating key system functionalityutilizing an encoder and decoder pair.

FIG. 17 is a method diagram illustrating possible use of a hybridencoder/decoder to improve the compression ratio.

FIG. 18 is a flow diagram illustrating the use of a data encoding systemused to recursively encode data to further reduce data size.

FIG. 19 is an exemplary system architecture of a data encoding systemused for cyber security purposes.

FIG. 20 is a flow diagram of an exemplary method used to detectanomalies in received encoded data and producing a warning.

FIG. 21 is a flow diagram of a data encoding system used for DistributedDenial of Service (DDoS) attack denial.

FIG. 22 is an exemplary system architecture of a data encoding systemused for data mining and analysis purposes.

FIG. 23 is a flow diagram of an exemplary method used to enablehigh-speed data mining of repetitive data.

FIG. 24 is an exemplary system architecture of a data encoding systemused for remote software and firmware updates.

FIG. 25 is a flow diagram of an exemplary method used to encode andtransfer software and firmware updates to a device for installation, forthe purposes of reduced bandwidth consumption.

FIG. 26 is an exemplary system architecture of a data encoding systemused for large-scale software installation such as operating systems.

FIG. 27 is a flow diagram of an exemplary method used to encode newsoftware and operating system installations for reduced bandwidthrequired for transference.

FIG. 28 is a block diagram illustrating an exemplary hardwarearchitecture of a computing device.

FIG. 29 is a block diagram illustrating an exemplary logicalarchitecture for a client device.

FIG. 30 is a block diagram showing an exemplary architecturalarrangement of clients, servers, and external services.

FIG. 31 is another block diagram illustrating an exemplary hardwarearchitecture of a computing device.

FIG. 32 is a method diagram illustrating a series of possible stepstaken for further obfuscating a codebook and collection of source databetween cryptographic endpoints, for increased hardness againstintrusion or attack, according to an aspect.

FIG. 33 is another method diagram illustrating a series of possiblesteps taken for further obfuscating a codebook and collection of sourcedata between cryptographic endpoints, for increased hardness againstintrusion or attack, according to an aspect.

FIG. 34 is a block diagram illustrating an exemplary system architecturefor a high-bandwidth encoding of genomic data system, according tovarious embodiments.

FIG. 35 is a method diagram of an exemplary method for scanning asequence dataset, according to some embodiments.

FIGS. 36A-D are exemplary genome graphs that may be processed by asystem for bandwidth-efficient encoding of genomic data, according tosome embodiments.

DETAILED DESCRIPTION

The inventor has conceived, and reduced to practice, a system andmethods for bandwidth-efficient encoding of genome and bioinformaticsequence datasets comprising a sequence analyzer configured to: analyzea received sequence dataset to determine a sequence dataset file type,scan the sequence dataset to maintain a count of unique characterscontained therein, identify positions where the unique character countincreases by a power of two, deconstruct the sequence dataset into aplurality of sourceblocks at the identified positions, and encode theplurality of sourceblocks using a data deconstruction engine and librarymanagement module to assign each sourceblock a reference code.

Genomic data and the analysis thereof is useful because it allows formany breakthroughs in various scientific industries due to the continuedresearch and experimentation regarding genes and genetics, both humanand animal alike. Because of the structural nature of genomic data(e.g., its repeatability and length), as well as its scientific valuefor researchers, it would be beneficial to have a system and method forefficiently transmitting and storing compacted genomic datasets. Such asystem and method would allow for scientists and researchers to storeand share datasets between and among themselves by greatly reducing theamount of data through one or more data compaction techniques describedherein.

Researchers are expected to share human genomic data according toconsent provided by the research participants. Genomic data aretypically shared with the scientific community through data resources,which can be accessed in three ways. The first way, open-access orunrestricted access is the broadest form of data sharing. Data areavailable to the public for any research purpose. The next way is withregistered access, which falls in between open-access andcontrolled-access. Researchers can obtain the data for any purpose:however, they must register their information, and their work with thedata may be monitored. The final way to access is throughcontrolled-access which requires researchers to describe their researchpurpose so that a special data access committee can evaluate theconsistency of the research purpose with the participant's consent. Theresearcher can only access the data after receiving the committee'sapproval. Clearly, there is a need and requirement for genomic data tobe kept private with restricted access. The disclosed system and method,their embodiments and various aspects, can provide data compaction,which is inherently made secure during the encoding process, while alsoproviding various other techniques for enhancing the data securityaspects described herein.

A common method of storing and visualizing genomic data is with the useof genome graphs. Various types of genome graphs that may be processedand analyzed using the disclosed system and method can include DeBruijin graphs, directed acyclic graphs, bidirected graph (i.e.,sequence graph), and biedged graphs (i.e., biedged sequence graphs).Each of these types of genome graphs has pros and cons associated withthe structure of the graph and what that type of information is conveyedcan depend on the graph structure. Genome graphs include the referencegenome together with genetic variation and polymorphic haplotypes.Typically, genome graphs also comprise metadata that describe the samplethe DNA/RNA sequence was obtained from. For example, a graph or itsnodes may have metadata present that describe the organism, the cellline, and the library-preparation method, among other descriptors. Theinclusion of metadata is important because it provides deeperinformation and allows researchers to make better use of the data.

The disclosed system can provide a specially configured computing devicewhich can capture the structure and data contained within a datasequence set and/or genome graph, encode and compact the sequence data,and store and/or transmit the compacted sequence data in a bandwidthefficient manner that improves upon the state of the art.

One or more different aspects may be described in the presentapplication. Further, for one or more of the aspects described herein,numerous alternative arrangements may be described; it should beappreciated that these are presented for illustrative purposes only andare not limiting of the aspects contained herein or the claims presentedherein in any way. One or more of the arrangements may be widelyapplicable to numerous aspects, as may be readily apparent from thedisclosure. In general, arrangements are described in sufficient detailto enable those skilled in the art to practice one or more of theaspects, and it should be appreciated that other arrangements may beutilized and that structural, logical, software, electrical and otherchanges may be made without departing from the scope of the particularaspects. Particular features of one or more of the aspects describedherein may be described with reference to one or more particular aspectsor figures that form a part of the present disclosure, and in which areshown, by way of illustration, specific arrangements of one or more ofthe aspects. It should be appreciated, however, that such features arenot limited to usage in the one or more particular aspects or figureswith reference to which they are described. The present disclosure isneither a literal description of all arrangements of one or more of theaspects nor a listing of features of one or more of the aspects thatmust be present in all arrangements.

Headings of sections provided in this patent application and the titleof this patent application are for convenience only, and are not to betaken as limiting the disclosure in any way.

Devices that are in communication with each other need not be incontinuous communication with each other, unless expressly specifiedotherwise. In addition, devices that are in communication with eachother may communicate directly or indirectly through one or morecommunication means or intermediaries, logical or physical.

A description of an aspect with several components in communication witheach other does not imply that all such components are required. To thecontrary, a variety of optional components may be described toillustrate a wide variety of possible aspects and in order to more fullyillustrate one or more aspects. Similarly, although process steps,method steps, algorithms or the like may be described in a sequentialorder, such processes, methods and algorithms may generally beconfigured to work in alternate orders, unless specifically stated tothe contrary. In other words, any sequence or order of steps that may bedescribed in this patent application does not, in and of itself,indicate a requirement that the steps be performed in that order. Thesteps of described processes may be performed in any order practical.Further, some steps may be performed simultaneously despite beingdescribed or implied as occurring non-simultaneously (e.g., because onestep is described after the other step). Moreover, the illustration of aprocess by its depiction in a drawing does not imply that theillustrated process is exclusive of other variations and modificationsthereto, does not imply that the illustrated process or any of its stepsare necessary to one or more of the aspects, and does not imply that theillustrated process is preferred. Also, steps are generally describedonce per aspect, but this does not mean they must occur once, or thatthey may only occur once each time a process, method, or algorithm iscarried out or executed. Some steps may be omitted in some aspects orsome occurrences, or some steps may be executed more than once in agiven aspect or occurrence.

When a single device or article is described herein, it will be readilyapparent that more than one device or article may be used in place of asingle device or article. Similarly, where more than one device orarticle is described herein, it will be readily apparent that a singledevice or article may be used in place of the more than one device orarticle.

The functionality or the features of a device may be alternativelyembodied by one or more other devices that are not explicitly describedas having such functionality or features. Thus, other aspects need notinclude the device itself.

Techniques and mechanisms described or referenced herein will sometimesbe described in singular form for clarity. However, it should beappreciated that particular aspects may include multiple iterations of atechnique or multiple instantiations of a mechanism unless notedotherwise. Process descriptions or blocks in figures should beunderstood as representing modules, segments, or portions of code whichinclude one or more executable instructions for implementing specificlogical functions or steps in the process. Alternate implementations areincluded within the scope of various aspects in which, for example,functions may be executed out of order from that shown or discussed,including substantially concurrently or in reverse order, depending onthe functionality involved, as would be understood by those havingordinary skill in the art.

Definitions

The term “bit” refers to the smallest unit of information that can bestored or transmitted. It is in the form of a binary digit (either 0 or1). In terms of hardware, the bit is represented as an electrical signalthat is either off (representing 0) or on (representing 1).

The term “byte” refers to a series of bits exactly eight bits in length.

The term “codebook” refers to a database containing sourceblocks eachwith a pattern of bits and reference code unique within that library.The terms “library” and “encoding/decoding library” are synonymous withthe term codebook.

The terms “compression” and “deflation” as used herein mean therepresentation of data in a more compact form than the original dataset.Compression and/or deflation may be either “lossless”, in which the datacan be reconstructed in its original form without any loss of theoriginal data, or “lossy” in which the data can be reconstructed in itsoriginal form, but with some loss of the original data.

The terms “compression factor” and “deflation factor” as used hereinmean the net reduction in size of the compressed data relative to theoriginal data (e.g., if the new data is 70% of the size of the original,then the deflation/compression factor is 30% or 0.3.)

The terms “compression ratio” and “deflation ratio”, and as used hereinall mean the size of the original data relative to the size of thecompressed data (e.g., if the new data is 70% of the size of theoriginal, then the deflation/compression ratio is 70% or 0.7.)

The term “data” means information in any computer-readable form.

The term “data set” refers to a grouping of data for a particularpurpose. One example of a data set might be a word processing filecontaining text and formatting information.

The term “effective compression” or “effective compression ratio” refersto the additional amount data that can be stored using the method hereindescribed versus conventional data storage methods. Although the methodherein described is not data compression, per se, expressing theadditional capacity in terms of compression is a useful comparison.

The term “sourcepacket” as used herein means a packet of data receivedfor encoding or decoding. A sourcepacket may be a portion of a data set.

The term “sourceblock” as used herein means a defined number of bits orbytes used as the block size for encoding or decoding. A sourcepacketmay be divisible into a number of sourceblocks. As one non-limitingexample, a 1 megabyte sourcepacket of data may be encoded using 512 bytesourceblocks. The number of bits in a sourceblock may be dynamicallyoptimized by the system during operation. In one aspect, a sourceblockmay be of the same length as the block size used by a particular filesystem, typically 512 bytes or 4,096 bytes.

The term “codeword” refers to the reference code form in which data isstored or transmitted in an aspect of the system. A codeword consists ofa reference code to a sourceblock in the library plus an indication ofthat sourceblock's location in a particular data set.

The term “alphabet” refers to the total group of unique characters foundwithin a sequence dataset to be encoded. For example, an exemplarydataset may comprise only the characters of “ABCD”, which means thealphabet for this dataset has a size of four because there are fourunique characters contained in the dataset, and the dataset's alphabetis described as “A, B, C, D”.

Conceptual Architecture

FIG. 1 is a diagram showing an embodiment 100 of the system in which allcomponents of the system are operated locally. As incoming data 101 isreceived by data deconstruction engine 102. Data deconstruction engine102 breaks the incoming data into sourceblocks, which are then sent tolibrary manager 103. Using the information contained in sourceblocklibrary lookup table 104 and sourceblock library storage 105, librarymanager 103 returns reference codes to data deconstruction engine 102for processing into codewords, which are stored in codeword storage 106.When a data retrieval request 107 is received, data reconstructionengine 108 obtains the codewords associated with the data from codewordstorage 106, and sends them to library manager 103. Library manager 103returns the appropriate sourceblocks to data reconstruction engine 108,which assembles them into the proper order and sends out the data in itsoriginal form 109.

FIG. 2 is a diagram showing an embodiment of one aspect 200 of thesystem, specifically data deconstruction engine 201. Incoming data 202is received by data analyzer 203, which optimally analyzes the databased on machine learning algorithms and input 204 from a sourceblocksize optimizer, which is disclosed below. Data analyzer may optionallyhave access to a sourceblock cache 205 of recently-processedsourceblocks, which can increase the speed of the system by avoidingprocessing in library manager 103. Based on information from dataanalyzer 203, the data is broken into sourceblocks by sourceblockcreator 206, which sends sourceblocks 207 to library manager 203 foradditional processing. Data deconstruction engine 201 receives referencecodes 208 from library manager 103, corresponding to the sourceblocks inthe library that match the sourceblocks sent by sourceblock creator 206,and codeword creator 209 processes the reference codes into codewordscomprising a reference code to a sourceblock and a location of thatsourceblock within the data set. The original data may be discarded, andthe codewords representing the data are sent out to storage 210.

FIG. 3 is a diagram showing an embodiment of another aspect of system300, specifically data reconstruction engine 301. When a data retrievalrequest 302 is received by data request receiver 303 (in the form of aplurality of codewords corresponding to a desired final data set), itpasses the information to data retriever 304, which obtains therequested data 305 from storage. Data retriever 304 sends, for eachcodeword received, a reference codes from the codeword 306 to librarymanager 103 for retrieval of the specific sourceblock associated withthe reference code. Data assembler 308 receives the sourceblock 307 fromlibrary manager 103 and, after receiving a plurality of sourceblockscorresponding to a plurality of codewords, assembles them into theproper order based on the location information contained in eachcodeword (recall each codeword comprises a sourceblock reference codeand a location identifier that specifies where in the resulting data setthe specific sourceblock should be restored to. The requested data isthen sent to user 309 in its original form.

FIG. 4 is a diagram showing an embodiment of another aspect of thesystem 400, specifically library manager 401. One function of librarymanager 401 is to generate reference codes from sourceblocks receivedfrom data deconstruction engine 301. As sourceblocks are received 402from data deconstruction engine 301, sourceblock lookup engine 403checks sourceblock library lookup table 404 to determine whether thosesourceblocks already exist in sourceblock library storage 105. If aparticular sourceblock exists in sourceblock library storage 105,reference code return engine 405 sends the appropriate reference code406 to data deconstruction engine 301. If the sourceblock does not existin sourceblock library storage 105, optimized reference code generator407 generates a new, optimized reference code based on machine learningalgorithms. Optimized reference code generator 407 then saves thereference code 408 to sourceblock library lookup table 104; saves theassociated sourceblock 409 to sourceblock library storage 105; andpasses the reference code to reference code return engine 405 forsending 406 to data deconstruction engine 301. Another function oflibrary manager 401 is to optimize the size of sourceblocks in thesystem. Based on information 411 contained in sourceblock library lookuptable 104, sourceblock size optimizer 410 dynamically adjusts the sizeof sourceblocks in the system based on machine learning algorithms andoutputs that information 412 to data analyzer 203. Another function oflibrary manager 401 is to return sourceblocks associated with referencecodes received from data reconstruction engine 301. As reference codesare received 414 from data reconstruction engine 301, reference codelookup engine 413 checks sourceblock library lookup table 415 toidentify the associated sourceblocks; passes that information tosourceblock retriever 416, which obtains the sourceblocks 417 fromsourceblock library storage 105; and passes them 418 to datareconstruction engine 301.

FIG. 5 is a diagram showing another embodiment of system 500, in whichdata is transferred between remote locations. As incoming data 501 isreceived by data deconstruction engine 502 at Location 1, datadeconstruction engine 301 breaks the incoming data into sourceblocks,which are then sent to library manager 503 at Location 1. Using theinformation contained in sourceblock library lookup table 504 atLocation 1 and sourceblock library storage 505 at Location 1, librarymanager 503 returns reference codes to data deconstruction engine 301for processing into codewords, which are transmitted 506 to datareconstruction engine 507 at Location 2. In the case where the referencecodes contained in a particular codeword have been newly generated bylibrary manager 503 at Location 1, the codeword is transmitted alongwith a copy of the associated sourceblock. As data reconstruction engine507 at Location 2 receives the codewords, it passes them to librarymanager module 508 at Location 2, which looks up the sourceblock insourceblock library lookup table 509 at Location 2, and retrieves theassociated from sourceblock library storage 510. Where a sourceblock hasbeen transmitted along with a codeword, the sourceblock is stored insourceblock library storage 510 and sourceblock library lookup table 504is updated. Library manager 503 returns the appropriate sourceblocks todata reconstruction engine 507, which assembles them into the properorder and sends the data in its original form 511.

FIG. 6 is a diagram showing an embodiment 600 in which a standardizedversion of a sourceblock library 603 and associated algorithms 604 wouldbe encoded as firmware 602 on a dedicated processing chip 601 includedas part of the hardware of a plurality of devices 600. Contained ondedicated chip 601 would be a firmware area 602, on which would bestored a copy of a standardized sourceblock library 603 anddeconstruction/reconstruction algorithms 604 for processing the data.Processor 605 would have both inputs 606 and outputs 607 to otherhardware on the device 600. Processor 605 would store incoming data forprocessing on on-chip memory 608, process the data using standardizedsourceblock library 603 and deconstruction/reconstruction algorithms604, and send the processed data to other hardware on device 600. Usingthis embodiment, the encoding and decoding of data would be handled bydedicated chip 601, keeping the burden of data processing off device's600 primary processors. Any device equipped with this embodiment wouldbe able to store and transmit data in a highly optimized,bandwidth-efficient format with any other device equipped with thisembodiment.

FIG. 12 is a diagram showing an exemplary system architecture 1200,according to a preferred embodiment of the invention. Incoming trainingdata sets may be received at a customized library generator 1300 thatprocesses training data to produce a customized word library 1201comprising key-value pairs of data words (each comprising a string ofbits) and their corresponding calculated binary Huffman codewords. Theresultant word library 1201 may then be processed by a library optimizer1400 to reduce size and improve efficiency, for example by pruninglow-occurrence data entries or calculating approximate codewords thatmay be used to match more than one data word. A transmissionencoder/decoder 1500 may be used to receive incoming data intended forstorage or transmission, process the data using a word library 1201 toretrieve codewords for the words in the incoming data, and then appendthe codewords (rather than the original data) to an outbound datastream. Each of these components is described in greater detail below,illustrating the particulars of their respective processing and otherfunctions, referring to FIGS. 2-4 .

System 1200 provides near-instantaneous source coding that isdictionary-based and learned in advance from sample training data, sothat encoding and decoding may happen concurrently with datatransmission. This results in computational latency that is near zerobut the data size reduction is comparable to classical compression. Forexample, if N bits are to be transmitted from sender to receiver, thecompression ratio of classical compression is C the ratio between thedeflation factor of system 1200 and that of multi-pass source coding isp, the classical compression encoding rate is R_(C) bits and thedecoding rate is R_(D) bit/s, and the transmission speed is S bit/s, thecompress-send-decompress time will be

$T_{old} = {\frac{N}{R_{C}} + \frac{N}{CS} + \frac{N}{CR_{D}}}$while the transmit-while-coding time for system 1200 will be (assumingthat encoding and decoding happen at least as quickly as networklatency):

$T_{new} = \frac{N_{p}}{CS}$so that the total data transit time improvement factor is

$\frac{T_{old}}{T_{new}} = \frac{\frac{CS}{R_{C}} + 1 + \frac{S}{R_{D}}}{p}$which presents a savings whenever

${\frac{CS}{R_{C}} + \frac{S}{R_{D}}} > {p - {1.}}$This is a reasonable scenario given that typical values in real-worldpractice are C=0.32, R_(C)=1.1·10¹², R_(D)=4.2·10¹², S=10¹¹, giving

${{\frac{CS}{R_{C}} + \frac{S}{R_{D}}} = {0.053\ldots}},$such that system 1200 will outperform the total transit time of the bestcompression technology available as long as its deflation factor is nomore than 5% worse than compression. Such customized dictionary-basedencoding will also sometimes exceed the deflation ratio of classicalcompression, particularly when network speeds increase beyond 100 Gb/s.

The delay between data creation and its readiness for use at a receivingend will be equal to only the source word length t (typically 5-15bytes), divided by the deflation factor C/p and the network speed S,i.e.

${delay}_{invention} = \frac{tp}{CS}$since encoding and decoding occur concurrently with data transmission.On the other hand, the latency associated with classical compression is

${delay}_{priorart} = {\frac{N}{R_{C}} + \frac{N}{CS} + \frac{N}{CR_{D}}}$where N is the packet/file size. Even with the generous values chosenabove as well as N=512K, t=10, and p=1.05, this results indelay_(invention)≈3.3·10⁻¹⁰ while delay_(priorart)≈1.3·10⁻⁷, a more than400-fold reduction in latency.

A key factor in the efficiency of Huffman coding used by system 1200 isthat key-value pairs be chosen carefully to minimize expected codinglength, so that the average deflation/compression ratio is minimized. Itis possible to achieve the best possible expected code length among allinstantaneous codes using Huffman codes if one has access to the exactprobability distribution of source words of a given desired length fromthe random variable generating them. In practice this is impossible, asdata is received in a wide variety of formats and the random processesunderlying the source data are a mixture of human input, unpredictable(though in principle, deterministic) physical events, and noise. System1200 addresses this by restriction of data types and density estimation;training data is provided that is representative of the type of dataanticipated in “real-world” use of system 1200, which is then used tomodel the distribution of binary strings in the data in order to build aHuffman code word library 1200.

FIG. 13 is a diagram showing a more detailed architecture for acustomized library generator 1300. When an incoming training data set1301 is received, it may be analyzed using a frequency creator 1302 toanalyze for word frequency (that is, the frequency with which a givenword occurs in the training data set). Word frequency may be analyzed byscanning all substrings of bits and directly calculating the frequencyof each substring by iterating over the data set to produce anoccurrence frequency, which may then be used to estimate the rate ofword occurrence in non-training data. A first Huffman binary tree iscreated based on the frequency of occurrences of each word in the firstdataset, and a Huffman codeword is assigned to each observed word in thefirst dataset according to the first Huffman binary tree. Machinelearning may be utilized to improve results by processing a number oftraining data sets and using the results of each training set to refinethe frequency estimations for non-training data, so that the estimationyield better results when used with real-world data (rather than, forexample, being only based on a single training data set that may not bevery similar to a received non-training data set). A second Huffman treecreator 1303 may be utilized to identify words that do not match anyexisting entries in a word library 1201 and pass them to a hybridencoder/decoder 1304, that then calculates a binary Huffman codeword forthe mismatched word and adds the codeword and original data to the wordlibrary 1201 as a new key-value pair. In this manner, customized librarygenerator 1300 may be used both to establish an initial word library1201 from a first training set, as well as expand the word library 1201using additional training data to improve operation.

FIG. 14 is a diagram showing a more detailed architecture for a libraryoptimizer 1400. A pruner 1401 may be used to load a word library 1201and reduce its size for efficient operation, for example by sorting theword library 1201 based on the known occurrence probability of eachkey-value pair and removing low-probability key-value pairs based on aloaded threshold parameter. This prunes low-value data from the wordlibrary to trim the size, eliminating large quantities ofvery-low-frequency key-value pairs such as single-occurrence words thatare unlikely to be encountered again in a data set. Pruning eliminatesthe least-probable entries from word library 1201 up to a giventhreshold, which will have a negligible impact on the deflation factorsince the removed entries are only the least-common ones, while theimpact on word library size will be larger because samples drawn fromasymptotically normal distributions (such as the log-probabilities ofwords generated by a probabilistic finite state machine, a modelwell-suited to a wide variety of real-world data) which occur in tailsof the distribution are disproportionately large in counting measure. Adelta encoder 1402 may be utilized to apply delta encoding to aplurality of words to store an approximate codeword as a value in theword library, for which each of the plurality of source words is a validcorresponding key. This may be used to reduce library size by replacingnumerous key-value pairs with a single entry for the approximatecodeword and then represent actual codewords using the approximatecodeword plus a delta value representing the difference between theapproximate codeword and the actual codeword. Approximate coding isoptimized for low-weight sources such as Golomb coding, run-lengthcoding, and similar techniques. The approximate source words may bechosen by locality-sensitive hashing, so as to approximate Hammingdistance without incurring the intractability of nearest-neighbor-searchin Hamming space. A parametric optimizer 1403 may load configurationparameters for operation to optimize the use of the word library 1201during operation. Best-practice parameter/hyperparameter optimizationstrategies such as stochastic gradient descent, quasi-random gridsearch, and evolutionary search may be used to make optimal choices forall interdependent settings playing a role in the functionality ofsystem 1200. In cases where lossless compression is not required, thedelta value may be discarded at the expense of introducing some limitederrors into any decoded (reconstructed) data.

FIG. 15 is a diagram showing a more detailed architecture for atransmission encoder/decoder 1500. According to various arrangements,transmission encoder/decoder 1500 may be used to deconstruct data forstorage or transmission, or to reconstruct data that has been received,using a word library 1201. A library comparator 1501 may be used toreceive data comprising words or codewords, and compare against a wordlibrary 1201 by dividing the incoming stream into substrings of length tand using a fast hash to check word library 1201 for each substring. Ifa substring is found in word library 1201, the corresponding key/value(that is, the corresponding source word or codeword, according towhether the substring used in comparison was itself a word or codeword)is returned and appended to an output stream. If a given substring isnot found in word library 1201, a mismatch handler 1502 and hybridencoder/decoder 1503 may be used to handle the mismatch similarly tooperation during the construction or expansion of word library 1201. Amismatch handler 1502 may be utilized to identify words that do notmatch any existing entries in a word library 1201 and pass them to ahybrid encoder/decoder 1503, that then calculates a binary Huffmancodeword for the mismatched word and adds the codeword and original datato the word library 1201 as a new key-value pair. The newly-producedcodeword may then be appended to the output stream. In arrangementswhere a mismatch indicator is included in a received data stream, thismay be used to preemptively identify a substring that is not in wordlibrary 1201 (for example, if it was identified as a mismatch on thetransmission end), and handled accordingly without the need for alibrary lookup.

FIG. 19 is an exemplary system architecture of a data encoding systemused for cyber security purposes. Much like in FIG. 1 , incoming data101 to be deconstructed is sent to a data deconstruction engine 102,which may attempt to deconstruct the data and turn it into a collectionof codewords using a library manager 103. Codeword storage 106 serves tostore unique codewords from this process, and may be queried by a datareconstruction engine 108 which may reconstruct the original data fromthe codewords, using a library manager 103. However, a cybersecuritygateway 1900 is present, communicating in-between a library manager 103and a deconstruction engine 102, and containing an anomaly detector 1910and distributed denial of service (DDoS) detector 1920. The anomalydetector examines incoming data to determine whether there is adisproportionate number of incoming reference codes that do not matchreference codes in the existing library. A disproportionate number ofnon-matching reference codes may indicate that data is being receivedfrom an unknown source, of an unknown type, or contains unexpected(possibly malicious) data. If the disproportionate number ofnon-matching reference codes exceeds an established threshold orpersists for a certain length of time, the anomaly detector 1910 raisesa warning to a system administrator. Likewise, the DDoS detector 1920examines incoming data to determine whether there is a disproportionateamount of repetitive data. A disproportionate amount of repetitive datamay indicate that a DDoS attack is in progress. If the disproportionateamount of repetitive data exceeds an established threshold or persistsfor a certain length of time, the DDoS detector 1910 raises a warning toa system administrator. In this way, a data encoding system may detectand warn users of, or help mitigate, common cyber-attacks that resultfrom a flow of unexpected and potentially harmful data, or attacks thatresult from a flow of too much irrelevant data meant to slow down anetwork or system, as in the case of a DDoS attack.

FIG. 22 is an exemplary system architecture of a data encoding systemused for data mining and analysis purposes. Much like in FIG. 1 ,incoming data 101 to be deconstructed is sent to a data deconstructionengine 102, which may attempt to deconstruct the data and turn it into acollection of codewords using a library manager 103. Codeword storage106 serves to store unique codewords from this process, and may bequeried by a data reconstruction engine 108 which may reconstruct theoriginal data from the codewords, using a library manager 103. A dataanalysis engine 2210, typically operating while the system is otherwiseidle, sends requests for data to the data reconstruction engine 108,which retrieves the codewords representing the requested data fromcodeword storage 106, reconstructs them into the data represented by thecodewords, and send the reconstructed data to the data analysis engine2210 for analysis and extraction of useful data (i.e., data mining).Because the speed of reconstruction is significantly faster thandecompression using traditional compression technologies (i.e.,significantly less decompression latency), this approach makes datamining feasible. Very often, data stored using traditional compressionis not mined precisely because decompression lag makes it unfeasible,especially during shorter periods of system idleness. Increasing thespeed of data reconstruction broadens the circumstances under which datamining of stored data is feasible.

FIG. 24 is an exemplary system architecture of a data encoding systemused for remote software and firmware updates. Software and firmwareupdates typically require smaller, but more frequent, file transfers. Aserver which hosts a software or firmware update 2410 may host anencoding-decoding system 2420, allowing for data to be encoded into, anddecoded from, sourceblocks or codewords, as disclosed in previousfigures. Such a server may possess a software update, operating systemupdate, firmware update, device driver update, or any other form ofsoftware update, which in some cases may be minor changes to a file, butnevertheless necessitate sending the new, completed file to therecipient. Such a server is connected over a network 2430, which isfurther connected to a recipient computer 2440, which may be connectedto a server 2410 for receiving such an update to its system. In thisinstance, the recipient device 2440 also hosts the encoding and decodingsystem 2450, along with a codebook or library of reference codes thatthe hosting server 2410 also shares. The updates are retrieved fromstorage at the hosting server 2410 in the form of codewords, transferredover the network 2430 in the form of codewords, and reconstructed on thereceiving computer 2440. In this way, a far smaller file size, andsmaller total update size, may be sent over a network. The receivingcomputer 2440 may then install the updates on any number of targetcomputing devices 2460 a-n, using a local network or otherhigh-bandwidth connection.

FIG. 26 is an exemplary system architecture of a data encoding systemused for large-scale software installation such as operating systems.Large-scale software installations typically require very large, butinfrequent, file transfers. A server which hosts an installable software2610 may host an encoding-decoding system 2620, allowing for data to beencoded into, and decoded from, sourceblocks or codewords, as disclosedin previous figures. The files for the large scale software installationare hosted on the server 2610, which is connected over a network 2630 toa recipient computer 2640. In this instance, the encoding and decodingsystem 2650 a-n is stored on or connected to one or more target devices2660 a-n, along with a codebook or library of reference codes that thehosting server 2610 shares. The software is retrieved from storage atthe hosting server 2610 in the form of codewords, and transferred overthe network 2630 in the form of codewords to the receiving computer2640. However, instead of being reconstructed at the receiving computer2640, the codewords are transmitted to one or more target computingdevices, and reconstructed and installed directly on the target devices2660 a-n. In this way, a far smaller file size, and smaller total updatesize, may be sent over a network or transferred between computingdevices, even where the network 2630 between the receiving computer 2640and target devices 2660 a-n is low bandwidth, or where there are manytarget devices 2660 a-n.

FIG. 34 is a block diagram illustrating an exemplary system architecturefor a high-bandwidth encoding of genomic data system, according tovarious embodiments. According to an embodiment, the system 3400 maycomprise a sequence analyzer 3410, a data deconstruction engine 3420, acodeword storage 3430 data store, a library management module 3440, anda data reconstruction engine 3450. In some embodiments of system 3400,sequencing module 3460 may be integrated with system 3400 such thatoutputted decoded sequence data from reconstruction engine 3450 may befed directly into sequencing module 3460, which can comprise or make useof various sequencing technologies known in the art, at the location ofa researcher or scientist. System 3400 may be configured to receivesequence data 3401 (e.g., genomic and/or bioinformatic data, etc.), scanthe received sequence data to determine the amount of unique characters,indicate specific locations in sequence dataset 3401 where the charactercount increase, calculate and compare compaction ratios for differentsegments (i.e., sourceblocks), and optimally divide the receivedsequence data into a plurality of sourceblocks based on the results ofthe comparison. Then, data deconstruction engine 3420 may receive thesourceblocks, compacting and encoding each sourceblock with a referencecode, and store the reference code and an indication of the locationwithin the source sequence in which the encoded sourceblock isoriginally located as a codeword in codeword storage 3430. The result isthe compaction, encryption, and storage or transmission of a very largegenomic sequence dataset.

Systems and methods of the invention implement the encoding/decoding andcompaction techniques previously described in this written description.In preferred embodiments, the data compaction technique is implementedfor bioinformatic data. Bioinformatic data may be particularlywell-suited to the encoding and compaction technique because such data:is often generated over small alphabets (i.e., the total group ofcharacters found within a sequence dataset to be encoded); oftencontains many repetitive sequences; and is often very large (e.g.,gigabyte, terabyte, etc.). The encoding and compaction techniquesdescribed herein exploit those features to create a compaction methodthat, though applicable to any kind of data, is particularly effectivein compacting and encrypting bioinformatic data (e.g., sequencedatasets).

The codewords stored in codeword storage 3420 are the compacted andencoded form of the received sequence dataset, providing data securityand high levels of data compaction which greatly reduces the storagecapacity required to store large amounts of sequence data, such asgenomic sequences. Additionally, data reconstruction engine 3450 may bepresent which can receive codewords, decode the received codewords, andreconstruct the decoded sequence dataset back into its original formatwithout loss of data. As both data deconstruction engine 3420 and datareconstruction engine 3450 have access to a same reference codebookwhich contains a plurality of codeword pairs, each codeword paircomprising a sequence data sourceblock and its corresponding referencecode (and, in some embodiments, information about the location of thereference code within the sequence dataset), only codewords need betransmitted between the two engines at different locations, resulting inless bandwidth being required to transmit large sequence datasets.

Present in this embodiment, is a sequence analyzer 3410 which isconfigured to perform a variety of functions in order to prepare areceived sequence dataset for further processing by data deconstructionengine 3420. The encoding and decoding system and methods describedherein are applicable to a variety of formats of sequence data andgenomic data such as an organism's genome, a multiple sequencealignment, a set of sequence read data, or a directed graph representingrelationships among multiple genomic sequences. In some embodiments, thesequence or genomic data that is encoded comprises a graph representingat least a portion of a plurality of genomes. Since system 3400 may beapplied to very large datasets, such as sequence reads, sequencealignments, or genomic reference graphs, very large genomic datasets maybe encoded very compactly.

According to some embodiments, sequence analyzer 3410 may comprise asequence parser 3412 configured to parse the sequence dataset toidentify the format of the dataset. The format of the sequence datasetmay be a text file (e.g., word document), a comma separated variable(CSV) format, a graphical format, a FASTA format, or other file formatfor storing genomic data known to those skilled in the art. Sequenceparser 3412 may determine what type of file is received by analyzing thereceived datasets binaries and/or actual code, and may make use of adatabase (not shown) of basic file type definitions which are used toencode files into a particular file format. If the received sequencedataset is determined to be a non-graphical format, then the sequencedataset may be scanned through in its entirety in order to determine thenumber of unique characters contained within the sequence dataset.System 3400 may maintain a count of the number of unique charactersfound within a received dataset, iterating through a dataset andincreasing a character count value each time a unique (that is,previously unencountered during the scan) character is encountered. Thischaracter count represents the size of the alphabet associated withreceived sequence dataset and may be used by system 3400 in order todetermine locations in the sequence dataset where the number of uniquecharacters increase by a power of two. These positions where the uniquecharacter count increases may be selected by system 3400 as the locationin the dataset where division into a plurality of sourceblocks wouldresult in efficient data compaction.

Once a received sequence dataset has been scanned resulting in a uniquecharacter count and an indication to the locations where the uniquecharacter count has increased by a power of two, compaction calculator3416 may then calculate the compaction ratio that would be obtained bydividing the sequence data into one of the plurality of sourceblocks atone of the indicated positions that result in the best compaction ratio.Once the most efficient positions have been determined, a sourceblockcreator 3418 may receive the sequence dataset 3401 and optimallydeconstruct the sequence dataset into a plurality of sourceblocks atthose positions. The plurality of sourceblocks may be sent to datadeconstruction engine 3420 for compaction and encoding actions.

If the received sequence dataset is determined to be a graphicalrepresentation (e.g., DAG, bidirectional, etc.), then the sequencedataset can be sent to a graph traversal engine 3414 which may select,from a plurality of graph traversal algorithms, an appropriate graphtraversal algorithm for the type of graph. Examples of graph traversalalgorithms that may be stored and operable on the graph traversal engine3414 may include, but are not limited to, depth-first search,breadth-first search, and greedy algorithm. In some embodiments, abreadth-first search algorithm may be set as the default graph traversalalgorithm because it is used to visit all the nodes of a given graph. Inthis traversal algorithm, one node is selected and then all the adjacentnodes are visited one by one. After completing all of the adjacentvertices, it moves further to check another vertex and checks itsadjacent vertices again. The selected (or default) graph traversalalgorithm may be used to first identify the reference sequence (orreference node) associated with the sequence dataset. Once identified,the reference sequence is assigned a first index value, and allsubsequent graph nodes are assigned an index value based on theirrelationship to the reference sequence. Due to the structure of graphs,graph traversal algorithms may visit the same vertex more than once. Aseach vertex in the graph is traversed, graph traversal engine 3414 mayflag (or make use of some other signifier) each vertex as it is indexedin order to prevent the same vertex being indexed more than once.Indexing of vertices may be performed in such a way that a vertices thatare connected by an edge may be assigned indexes close in value. Forexample, if vertex 1 is the reference sequence then it would be assignedindex value 001, and it is connected to vertex 2, then vertex two wouldbe assigned index value 002. The indexing of the graphical sequence datais necessary for capturing the topology of and the relationshipscontained within a genome graph. The assigned index value can be storedwith the encoded sequence data (as part of the codeword), filling asimilar role to the location indicator which is stored in the codebookcreated for non-graphical sequence datasets; as the index values can beused by data reconstruction engine 3450 to decode and reassemble thegenomic graph data in the original format it was received in.

According to some embodiments, during a graph traversal operation aseach node is traversed, sequence analyzer 3410 maintains a count of theunique characters contained within the genome graph, in a way similardescribed above with respect to sequence parser 3412. Nodes of thegenome graph may be segmented based upon the unique character count andthe indication of where the character count increase by a power of two.These segmentations may be sent to compaction calculator 3416, which maybe configured to calculate and compare the compaction ratios associatedwith the received graph segmentations.

According to some embodiments, sequence analyzer 3410 may be configuredto scan incoming sequence data to identify one or more points along thesequence at which the alphabet size will change and flags (or otherwisenotes) those points at which the alphabet size will increase to a powerof two (e.g., go from 3 to 4 or from 7 to 8). The powers of two are therelevant break-points for various embodiments of the disclosed systemand method. Whenever one of these break-points is encountered bysequence analyzer 3410, compaction calculator 3416 calculates how muchspace it would take to encode the sequence with an alphabet of thatsize. The efficiency of the representation is then calculated, and theseefficiency values can be compared against each other in order todetermine the best alphabet size for a given region, thereby choosingbreak-points that optimize the compaction techniques. In someembodiments, these break-even points may be used to determine where andwhat size sourceblocks are to be selected and then sent to datadeconstruction engine 3420 for compaction and encryption.

As an example of the above described break even points, consider thisexemplary genomic sequence that can be processed by system 3400:NATGAC-GAGAGAGCA-TTTTTTT. This example is selected to illustratedifferent alphabet sizes that may be invoked with a particular division.The original genomic data is divided into three segments. The firstsegment includes NATGCA-. The second segment includes GAGAGAGCA-. Thethird segment includes TTTTTTT. The alphabet for the third segment is asingle character (“T”). The alphabet for the for the first segment sixcharacters (N, A, C, T, G, -) and thus requires 3 bits per character.The second segment has four characters and could be encoded using 2 bitsper character. Once the data has been divided, it is ready to be encodedby data deconstruction engine 3420 as described throughout thisdisclosure.

Description of Method Aspects

Since the library consists of re-usable building sourceblocks, and theactual data is represented by reference codes to the library, the totalstorage space of a single set of data would be much smaller thanconventional methods, wherein the data is stored in its entirety. Themore data sets that are stored, the larger the library becomes, and themore data can be stored in reference code form.

As an analogy, imagine each data set as a collection of printed booksthat are only occasionally accessed. The amount of physical shelf spacerequired to store many collections would be quite large, and isanalogous to conventional methods of storing every single bit of data inevery data set. Consider, however, storing all common elements withinand across books in a single library, and storing the books asreferences codes to those common elements in that library. As a singlebook is added to the library, it will contain many repetitions of wordsand phrases. Instead of storing the whole words and phrases, they areadded to a library, and given a reference code, and stored as referencecodes. At this scale, some space savings may be achieved, but thereference codes will be on the order of the same size as the wordsthemselves. As more books are added to the library, larger phrases,quotations, and other words patterns will become common among the books.The larger the word patterns, the smaller the reference codes will be inrelation to them as not all possible word patterns will be used. Asentire collections of books are added to the library, sentences,paragraphs, pages, or even whole books will become repetitive. There maybe many duplicates of books within a collection and across multiplecollections, many references and quotations from one book to another,and much common phraseology within books on particular subjects. If eachunique page of a book is stored only once in a common library and givena reference code, then a book of 1,000 pages or more could be stored ona few printed pages as a string of codes referencing the properfull-sized pages in the common library. The physical space taken up bythe books would be dramatically reduced. The more collections that areadded, the greater the likelihood that phrases, paragraphs, pages, orentire books will already be in the library, and the more information ineach collection of books can be stored in reference form. Accessingentire collections of books is then limited not by physical shelf space,but by the ability to reprint and recycle the books as needed for use.

The projected increase in storage capacity using the method hereindescribed is primarily dependent on two factors: 1) the ratio of thenumber of bits in a block to the number of bits in the reference code,and 2) the amount of repetition in data being stored by the system.

With respect to the first factor, the number of bits used in thereference codes to the sourceblocks must be smaller than the number ofbits in the sourceblocks themselves in order for any additional datastorage capacity to be obtained. As a simple example, 16-bitsourceblocks would require 216, or 65536, unique reference codes torepresent all possible patterns of bits. If all possible 65536 blockspatterns are utilized, then the reference code itself would also need tocontain sixteen bits in order to refer to all possible 65,536 blockspatterns. In such case, there would be no storage savings. However, ifonly 16 of those block patterns are utilized, the reference code can bereduced to 4 bits in size, representing an effective compression of 4times (16 bits/4 bits=4) versus conventional storage. Using a typicalblock size of 512 bytes, or 4,096 bits, the number of possible blockpatterns is 24,096, which for all practical purposes is unlimited. Atypical hard drive contains one terabyte (TB) of physical storagecapacity, which represents 1,953,125,000, or roughly 231, 512 byteblocks. Assuming that 1 TB of unique 512-byte sourceblocks werecontained in the library, and that the reference code would thus need tobe 31 bits long, the effective compression ratio for stored data wouldbe on the order of 132 times (4,096/31≈132) that of conventionalstorage.

With respect to the second factor, in most cases it could be assumedthat there would be sufficient repetition within a data set such that,when the data set is broken down into sourceblocks, its size within thelibrary would be smaller than the original data. However, it isconceivable that the initial copy of a data set could require somewhatmore storage space than the data stored in a conventional manner, if allor nearly all sourceblocks in that set were unique. For example,assuming that the reference codes are 1/10th the size of a full-sizedcopy, the first copy stored as sourceblocks in the library would need tobe 1.1 megabytes (MB), (1 MB for the complete set of full-sizedsourceblocks in the library and 0.1 MB for the reference codes).However, since the sourceblocks stored in the library are universal, themore duplicate copies of something you save, the greater efficiencyversus conventional storage methods. Conventionally, storing 10 copiesof the same data requires 10 times the storage space of a single copy.For example, ten copies of a 1 MB file would take up 10 MB of storagespace. However, using the method described herein, only a singlefull-sized copy is stored, and subsequent copies are stored as referencecodes. Each additional copy takes up only a fraction of the space of thefull-sized copy. For example, again assuming that the reference codesare 1/10th the size of the full-size copy, ten copies of a 1 MB filewould take up only 2 MB of space (1 MB for the full-sized copy, and 0.1MB each for ten sets of reference codes). The larger the library, themore likely that part or all of incoming data will duplicatesourceblocks already existing in the library.

The size of the library could be reduced in a manner similar to storageof data. Where sourceblocks differ from each other only by a certainnumber of bits, instead of storing a new sourceblock that is verysimilar to one already existing in the library, the new sourceblockcould be represented as a reference code to the existing sourceblock,plus information about which bits in the new block differ from theexisting block. For example, in the case where 512 byte sourceblocks arebeing used, if the system receives a new sourceblock that differs byonly one bit from a sourceblock already existing in the library, insteadof storing a new 512 byte sourceblock, the new sourceblock could bestored as a reference code to the existing sourceblock, plus a referenceto the bit that differs. Storing the new sourceblock as a reference codeplus changes would require only a few bytes of physical storage spaceversus the 512 bytes that a full sourceblock would require. Thealgorithm could be optimized to store new sourceblocks in this referencecode plus changes form unless the changes portion is large enough thatit is more efficient to store a new, full sourceblock.

It will be understood by one skilled in the art that transfer andsynchronization of data would be increased to the same extent as forstorage. By transferring or synchronizing reference codes instead offull-sized data, the bandwidth requirements for both types of operationsare dramatically reduced.

In addition, the method described herein is inherently a form ofencryption. When the data is converted from its full form to referencecodes, none of the original data is contained in the reference codes.Without access to the library of sourceblocks, it would be impossible tore-construct any portion of the data from the reference codes. Thisinherent property of the method described herein could obviate the needfor traditional encryption algorithms, thereby offsetting most or all ofthe computational cost of conversion of data back and forth to referencecodes. In theory, the method described herein should not utilize anyadditional computing power beyond traditional storage using encryptionalgorithms. Alternatively, the method described herein could be inaddition to other encryption algorithms to increase data security evenfurther.

In other embodiments, additional security features could be added, suchas: creating a proprietary library of sourceblocks for proprietarynetworks, physical separation of the reference codes from the library ofsourceblocks, storage of the library of sourceblocks on a removabledevice to enable easy physical separation of the library and referencecodes from any network, and incorporation of proprietary sequences ofhow sourceblocks are read and the data reassembled.

FIG. 7 is a diagram showing an example of how data might be convertedinto reference codes using an aspect of an embodiment 700. As data isreceived 701, it is read by the processor in sourceblocks of a sizedynamically determined by the previously disclosed sourceblock sizeoptimizer 410. In this example, each sourceblock is 16 bits in length,and the library 702 initially contains three sourceblocks with referencecodes 00, 01, and 10. The entry for reference code 11 is initiallyempty. As each 16 bit sourceblock is received, it is compared with thelibrary. If that sourceblock is already contained in the library, it isassigned the corresponding reference code. So, for example, as the firstline of data (0000 0011 0000 0000) is received, it is assigned thereference code (01) associated with that sourceblock in the library. Ifthat sourceblock is not already contained in the library, as is the casewith the third line of data (0000 1111 0000 0000) received in theexample, that sourceblock is added to the library and assigned areference code, in this case 11. The data is thus converted 703 to aseries of reference codes to sourceblocks in the library. The data isstored as a collection of codewords, each of which contains thereference code to a sourceblock and information about the location ofthe sourceblocks in the data set. Reconstructing the data is performedby reversing the process. Each stored reference code in a datacollection is compared with the reference codes in the library, thecorresponding sourceblock is read from the library, and the data isreconstructed into its original form.

FIG. 8 is a method diagram showing the steps involved in using anembodiment 800 to store data. As data is received 801, it would bedeconstructed into sourceblocks 802, and passed 803 to the librarymanagement module for processing. Reference codes would be received back804 from the library management module, and could be combined withlocation information to create codewords 805, which would then be stored806 as representations of the original data.

FIG. 9 is a method diagram showing the steps involved in using anembodiment 900 to retrieve data. When a request for data is received901, the associated codewords would be retrieved 902 from the library.The codewords would be passed 903 to the library management module, andthe associated sourceblocks would be received back 904. Upon receipt,the sourceblocks would be assembled 905 into the original data using thelocation data contained in the codewords, and the reconstructed datawould be sent out 906 to the requestor.

FIG. 10 is a method diagram showing the steps involved in using anembodiment 1000 to encode data. As sourceblocks are received 1001 fromthe deconstruction engine, they would be compared 1002 with thesourceblocks already contained in the library. If that sourceblockalready exists in the library, the associated reference code would bereturned 1005 to the deconstruction engine. If the sourceblock does notalready exist in the library, a new reference code would be created 1003for the sourceblock. The new reference code and its associatedsourceblock would be stored 1004 in the library, and the reference codewould be returned to the deconstruction engine.

FIG. 11 is a method diagram showing the steps involved in using anembodiment 1100 to decode data. As reference codes are received 1101from the reconstruction engine, the associated sourceblocks areretrieved 1102 from the library, and returned 1103 to the reconstructionengine.

FIG. 16 is a method diagram illustrating key system functionalityutilizing an encoder and decoder pair, according to a preferredembodiment. In a first step 1601, at least one incoming data set may bereceived at a customized library generator 1300 that then 1602 processesdata to produce a customized word library 1201 comprising key-valuepairs of data words (each comprising a string of bits) and theircorresponding calculated binary Huffman codewords. A subsequent datasetmay be received, and compared to the word library 1603 to determine theproper codewords to use in order to encode the dataset. Words in thedataset are checked against the word library and appropriate encodingsare appended to a data stream 1604. If a word is mismatched within theword library and the dataset, meaning that it is present in the datasetbut not the word library, then a mismatched code is appended, followedby the unencoded original word. If a word has a match within the wordlibrary, then the appropriate codeword in the word library is appendedto the data stream. Such a data stream may then be stored or transmitted1605 to a destination as desired. For the purposes of decoding, analready-encoded data stream may be received and compared 1606, andun-encoded words may be appended to a new data stream 1607 depending onword matches found between the encoded data stream and the word librarythat is present. A matching codeword that is found in a word library isreplaced with the matching word and appended to a data stream, and amismatch code found in a data stream is deleted and the followingunencoded word is re-appended to a new data stream, the inverse of theprocess of encoding described earlier. Such a data stream may then bestored or transmitted 1608 as desired.

FIG. 17 is a method diagram illustrating possible use of a hybridencoder/decoder to improve the compression ratio, according to apreferred aspect. A second Huffman binary tree may be created 1701,having a shorter maximum length of codewords than a first Huffman binarytree 1602, allowing a word library to be filled with every combinationof codeword possible in this shorter Huffman binary tree 1702. A wordlibrary may be filled with these Huffman codewords and words from adataset 1702, such that a hybrid encoder/decoder 1304, 1503 may receiveany mismatched words from a dataset for which encoding has beenattempted with a first Huffman binary tree 1703, 1604 and parsepreviously mismatched words into new partial codewords (that is,codewords that are each a substring of an original mismatched codeword)using the second Huffman binary tree 1704. In this way, an incompleteword library may be supplemented by a second word library. New codewordsattained in this way may then be returned to a transmission encoder1705, 1500. In the event that an encoded dataset is received fordecoding, and there is a mismatch code indicating that additional codingis needed, a mismatch code may be removed and the unencoded word used togenerate a new codeword as before 1706, so that a transmission encoder1500 may have the word and newly generated codeword added to its wordlibrary 1707, to prevent further mismatching and errors in encoding anddecoding.

It will be recognized by a person skilled in the art that the methodsdescribed herein can be applied to data in any form. For example, themethod described herein could be used to store genetic data, which hasfour data units (e.g., its alphabet has four characters): C, G, A, andT. Those four data units can be represented as 2 bit sequences: 00, 01,10, and 11, which can be processed and stored using the method describedherein.

It will be recognized by a person skilled in the art that certainembodiments of the methods described herein may have uses other thandata storage. For example, because the data is stored in reference codeform, it cannot be reconstructed without the availability of the libraryof sourceblocks. This is effectively a form of encryption, which couldbe used for cyber security purposes. As another example, an embodimentof the method described herein could be used to store backup copies ofdata, provide for redundancy in the event of server failure, or provideadditional security against cyberattacks by distributing multiplepartial copies of the library among computers are various locations,ensuring that at least two copies of each sourceblock exist in differentlocations within the network.

FIG. 18 is a flow diagram illustrating the use of a data encoding systemused to recursively encode data to further reduce data size. Data may beinput 1805 into a data deconstruction engine 102 to be deconstructedinto code references, using a library of code references based on theinput 1810. Such example data is shown in a converted, encoded format1815, highly compressed, reducing the example data from 96 bits of data,to 12 bits of data, before sending this newly encoded data through theprocess again 1820, to be encoded by a second library 1825, reducing iteven further. The newly converted data 1830 is shown as only 6 bits inthis example, thus a size of 6.25% of the original data packet. Withrecursive encoding, then, it is possible and implemented in the systemto achieve increasing compression ratios, using multi-layered encoding,through recursively encoding data. Both initial encoding libraries 1810and subsequent libraries 1825 may be achieved through machine learningtechniques to find optimal encoding patterns to reduce size, with thelibraries being distributed to recipients prior to transfer of theactual encoded data, such that only the compressed data 1830 must betransferred or stored, allowing for smaller data footprints andbandwidth requirements. This process can be reversed to reconstruct thedata. While this example shows only two levels of encoding, recursiveencoding may be repeated any number of times. The number of levels ofrecursive encoding will depend on many factors, a non-exhaustive list ofwhich includes the type of data being encoded, the size of the originaldata, the intended usage of the data, the number of instances of databeing stored, and available storage space for codebooks and libraries.Additionally, recursive encoding can be applied not only to data to bestored or transmitted, but also to the codebooks and/or libraries,themselves. For example, many installations of different libraries couldtake up a substantial amount of storage space. Recursively encodingthose different libraries to a single, universal library woulddramatically reduce the amount of storage space required, and eachdifferent library could be reconstructed as necessary to reconstructincoming streams of data.

FIG. 20 is a flow diagram of an exemplary method used to detectanomalies in received encoded data and producing a warning. A system mayhave trained encoding libraries 2010, before data is received from somesource such as a network connected device or a locally connected deviceincluding USB connected devices, to be decoded 2020. Decoding in thiscontext refers to the process of using the encoding libraries to takethe received data and attempt to use encoded references to decode thedata into its original source 2030, potentially more than once ifrecursive encoding was used, but not necessarily more than once. Ananomaly detector 1910 may be configured to detect a large amount ofun-encoded data 2040 in the midst of encoded data, by locating data orreferences that do not appear in the encoding libraries, indicating atleast an anomaly, and potentially data tampering or faulty encodinglibraries. A flag or warning is set by the system 2050, allowing a userto be warned at least of the presence of the anomaly and thecharacteristics of the anomaly. However, if a large amount of invalidreferences or unencoded data are not present in the encoded data that isattempting to be decoded, the data may be decoded and output as normal2060, indicating no anomaly has been detected.

FIG. 21 is a flow diagram of a method used for Distributed Denial ofService (DDoS) attack denial. A system may have trained encodinglibraries 2110, before data is received from some source such as anetwork connected device or a locally connected device including USBconnected devices, to be decoded 2120. Decoding in this context refersto the process of using the encoding libraries to take the received dataand attempt to use encoded references to decode the data into itsoriginal source 2130, potentially more than once if recursive encodingwas used, but not necessarily more than once. A DDoS detector 1920 maybe configured to detect a large amount of repeating data 2140 in theencoded data, by locating data or references that repeat many times over(the number of which can be configured by a user or administrator asneed be), indicating a possible DDoS attack. A flag or warning is set bythe system 2150, allowing a user to be warned at least of the presenceof a possible DDoS attack, including characteristics about the data andsource that initiated the flag, allowing a user to then block incomingdata from that source. However, if a large amount of repeat data in ashort span of time is not detected, the data may be decoded and outputas normal 2160, indicating no DDoS attack has been detected.

FIG. 23 is a flow diagram of an exemplary method used to enablehigh-speed data mining of repetitive data. A system may have trainedencoding libraries 2310, before data is received from some source suchas a network connected device or a locally connected device includingUSB connected devices, to be analyzed 2320 and decoded 2330. Whendetermining data for analysis, users may select specific data todesignate for decoding 2330, before running any data mining or analyticsfunctions or software on the decoded data 2340. Rather than havingtraditional decryption and decompression operate over distributeddrives, data can be regenerated immediately using the encoding librariesdisclosed herein, as it is being searched. Using methods described inFIG. 9 and FIG. 11 , data can be stored, retrieved, and decoded swiftlyfor searching, even across multiple devices, because the encodinglibrary may be on each device. For example, if a group of servers hostcodewords relevant for data mining purposes, a single computer canrequest these codewords, and the codewords can be sent to the recipientswiftly over the bandwidth of their connection, allowing the recipientto locally decode the data for immediate evaluation and searching,rather than running slow, traditional decompression algorithms on datastored across multiple devices or transfer larger sums of data acrosslimited bandwidth.

FIG. 25 is a flow diagram of an exemplary method used to encode andtransfer software and firmware updates to a device for installation, forthe purposes of reduced bandwidth consumption. A first system may havetrained code libraries or “codebooks” present 2510, allowing for asoftware update of some manner to be encoded 2520. Such a softwareupdate may be a firmware update, operating system update, securitypatch, application patch or upgrade, or any other type of softwareupdate, patch, modification, or upgrade, affecting any computer system.A codebook for the patch must be distributed to a recipient 2530, whichmay be done beforehand and either over a network or through a local orphysical connection, but must be accomplished at some point in theprocess before the update may be installed on the recipient device 2560.An update may then be distributed to a recipient device 2540, allowing arecipient with a codebook distributed to them 2530 to decode the update2550 before installation 2560. In this way, an encoded and thus heavilycompressed update may be sent to a recipient far quicker and with lessbandwidth usage than traditional lossless compression methods for data,or when sending data in uncompressed formats. This especially maybenefit large distributions of software and software updates, as withenterprises updating large numbers of devices at once.

FIG. 27 is a flow diagram of an exemplary method used to encode newsoftware and operating system installations for reduced bandwidthrequired for transference. A first system may have trained codelibraries or “codebooks” present 2710, allowing for a softwareinstallation of some manner to be encoded 2720. Such a softwareinstallation may be a software update, operating system, securitysystem, application, or any other type of software installation,execution, or acquisition, affecting a computer system. An encodinglibrary or “codebook” for the installation must be distributed to arecipient 2730, which may be done beforehand and either over a networkor through a local or physical connection, but must be accomplished atsome point in the process before the installation can begin on therecipient device 2760. An installation may then be distributed to arecipient device 2740, allowing a recipient with a codebook distributedto them 2730 to decode the installation 2750 before executing theinstallation 2760. In this way, an encoded and thus heavily compressedsoftware installation may be sent to a recipient far quicker and withless bandwidth usage than traditional lossless compression methods fordata, or when sending data in uncompressed formats. This especially maybenefit large distributions of software and software updates, as withenterprises updating large numbers of devices at once.

FIG. 32 is a method diagram illustrating a series of possible stepstaken for further obfuscating a codebook and collection of source databetween cryptographic endpoints, for increased hardness againstintrusion or attack, according to an aspect. First, source data must besplit into blocks of source data, or “sourceblocks,” for encoding 3210.This is a common first step for cryptographic block ciphers. The lengthof such blocks is paramount, as a block cipher switches sourceblocks ofa given length for a codeword of equal length. A plurality of possibleshuffling techniques may then be used on the source data, before orafter being initially encrypted, depending on which steps are enabled bythe encrypting endpoint. If key whitening is enabled, source data ispreprocessed by the initial endpoint in system to determine randomly orprogrammatically spaced codeword blocks of equal length, in place ofsource blocks 3220, before encrypting the entire collection of blocks,effectively causing the randomly or programmatically selected blocks tobecome double or n encrypted, requiring multiple decrypting steps torecover the original source material. This key whitening may insteadalso be used for XOR encrypting, in which either the originalsourceblock or a codeblock is sent in place of certain blocks, and thedecrypting endpoint decrypts with the same XOR pattern, such that anygiven cipher block may have at least two (but possibly more) versionsthat may be used, making intrusion or attacking the encryption moredifficult and costly, requiring the use of statistical models from theattackers.

“Key whitening” 3220 can be used to make attackers' task significantlyharder, by preprocessing all data before transmission via XOR (meaningeither the original data, or an alternative pre-processed cipher may beplaced in its place, before the data is encrypted) with a previouslyagreed-upon random key whose length is an integer divisor of thesourceblock length. It need only be a divisor of a small multiple of thesourceblock length, where the increased size of this multiplying factorwill increase the codebook size and introduce additional latency. Thesystem may be insensate to the contents of sourceblocks, and insteadrely solely on their frequencies. Thus, for example, if sourceblocks oflength 64 are XOR-ed with a separate shared key of length 64 beforetraining and also during encoding/decoding, attackers would have to usecomputationally expensive statistical attacks (or side-channel attacks,etc.) to obtain this key before the results of any codebook or keyattacks could be used to obtain any unencrypted data. This preprocessingkey may be updated regularly and communicated via public key encryptionor a secure channel between sender and receiver in order to thwartattackers without large amounts of time or computing resources at theirdisposal.

The codebook may also trained to be sent to opposing endpoint(s)containing key whitening codewords, if key whitening was enabled andutilized 3230, causing the codebook or codebooks used to becomeregenerated in a different state than before, further complicating thetask of attackers. If codebook regeneration is enabled in this way, thecodebook may be re-trained on new training data, salted data, or olddata that has merely bee rearranged, to produce a new codebook for newmessage(s) to be sent 3240 between the endpoints.

Because of the order-dependent and highly nonlinear nature of severalsubroutines of some learning processes, new sourceblock-codeword pairmappings may be very different each time a training process executes.These new codebooks, when pushed out to the transmitting and receivingdevices 3250, serve as fresh keys, frustrating attackers whose time andresources cracking keys will be largely wasted with each codebookupdate. Similar to using key whitening as described above, thissignificantly increases the difficulty of extracting keys and plaintextin order to compromise the privacy/security of AtomBeam-encoded data.

FIG. 33 is another method diagram illustrating a series of possiblesteps taken for further obfuscating a codebook and collection of sourcedata between cryptographic endpoints, for increased hardness againstintrusion or attack, according to an aspect. First, a user such as theinitial encrypting endpoint must enable codebook shuffling 3310, whichmay be enabled through a text or graphical user interface when using theencrypting system. The user may select two differing methods of codebookshuffling other than those previously disclosed, the first method beingan in-length permutation for shuffling in which an entirely new codebookmay be shared with the opposing endpoint or endpoints 3320.

All properties of the codebook, and the system that uses the codebook,are left unchanged if all codewords of a fixed length are permutedamongst themselves. Therefore, the sender and receiver would agree,perhaps via an encrypted communication, on one permutation per lengthwhen an update is triggered. That is, one endpoint (sender or receiver)will find the minimum codeword length m and the maximum codeword lengthM, then tally the number of codewords of each length: L(m), L(m+1), . .. , L(M). Then, it will generate a permutation by one of the methodsdescribed below for each such length: tau_m, tau_(m+1), . . . , tau_M,where tau_k is a function for a permutation of {1,2, . . . , L(k)}, i.e.{tau_k(1), . . . , tau_k(L(k))} is a reordering of {1,2, . . . , L(k)}.Then, the list of tau_j, j from m to M, may be securely transmitted tothe other endpoint. The sender, when they use the codebook, will look upthe sourceblock S in the codebook and find, for instance, that it is the“j-th” codeword of length L in the codebook, then transmit the tau_j(L)codeword among codewords of length L in the codebook. The receiver, uponreceipt of this codeword, looks it up in the codebook and finds that itis, for instance, the “T-th” codeword among codewords of length L in thecodebook, then may apply the inverse function of the tau's, i.e. findthe codeword of length L numbered inverse_tau_L(T) in the codebook,which will correspond to the sourceblock S. There is also a way to dothis less implicitly if the user can afford to store temporary codebooksinstead of using these permutations at runtime: for each j and L,replace the j-th codeword of length L in the encoding codebook with thecodeword numbered tau_L(j); in the decoding codebook, the T-thsourceblock corresponding to a codeword of length L is replaced with thesourceblock numbered inverse_tau_L(T). In this latter version, thedecoding codebook must be accompanied by the list of tau's, or at leastenough information to obtain the tau's, or else decoding will not bepossible.

As part of this first method of shuffling using functions to replacespecified codewords with alternatives, essentially utilizing a partialsecond-layer which is more difficult to attack than a full second-layerof encrypting since it is non-obvious which layer is which and whichcodewords are switched, several possible variations may exist.

If the new codebook is not shared or it is not desirable to share thenew codebook, specific ordering or characteristics of successivecodebook shuffles may be established between endpoints before data isexchanged, removing the need to share the entire codebook 3330, butdecreasing the strength of the shuffle from outside intrusion due to adecrease in the entropy of the shuffling. Using this variation, a set of“R tau” functions for each valid length L are agreed upon at thebeginning by the endpoints: tau_{L,1}, tau_{L,2}, . . . , tau_{L,R}. (Rcould vary between values of L.) Then, the endpoints agree with eachshuffle update on indices i_m, i_(m+1), . . . , i_M (chosen randomly),and use tau_{L,i_L} for the length-L permutation. This is slightly lesssecure than generating new tau_k functions for each permutation, butrequires much less data be computed and sent.

Alternatively, If ordering of shuffles is not shared, endpoints mayagree ahead of time on specific algorithms to run on codebook toshuffle, and then merely share an integer value showing how many timesto shuffle entire codebook or specific segments of codebook 3340. Forinstance, a set of tau's are agreed upon at the beginning by theendpoints, i.e. tau_m, tau_(m+1), . . . , tau_M. Then, the endpointsagree with each shuffle update on integers i_m, i_(m+1), . . . , i_M(chosen randomly), and use tau_L{circumflex over ( )}(i_L) for thelength-L permutation, where the exponent here denotes functionself-composition. That is, tau{circumflex over ( )}1(x)=tau,tau{circumflex over ( )}2(x)=tau(tau(x)), tau{circumflex over( )}3(x)=tau(tau(tau(x))), etc. This is an even less secure than theprevious option but requires even less data be sent.

If all previous methods of sharing data about codebook shuffling are notused, an alternative shuffle may involve endpoints sharing a range ofindices of codebook values to shuffle/scramble, and share an identifierfor the shuffle algorithm chosen as a parameterization of the dataexchange 3350. For instance, a parametric recipe for tau's are agreedupon at the beginning by the endpoints: f_m(j), . . . , f_M(j), wheref_r(j) is a permutation of {1, . . . , L(r)} for each j in some range ofindices. Then, the endpoints agree with each shuffle update on indicesi_m, . . . , i_M (chosen randomly) and use the permutationtau_L=f_L(i_L) for each L to permute the length-L codewords. Forexample, f_L(j) may be a single previously agreed upon permutation rho_Lplus j modulo L(r). For another example, f_L(j) may be multiplicationmodulo L(r) by the j-th invertible element of the ring of integersmodulo L(r). There are an infinitude of such recipes possible whichcould use exponentiation in modular arithmetic, standard card shufflepermutations, permutations arising as the order type of the sequence ofinteger multiples of an irrational modulo 1, etc. This method requirestransmitting and keeping track of the least amount of information, butadds the least amount of hardness to an intruder's interception task.

Alternatively, a different method of shuffling may be used, in which theuser may select in-length XOR for shuffling 3360. The endpoints couldagree on a set of binary words w_m, . . . , w_M of length m, m+1, . . ., M (see above for definitions of m and M) 3370. Then, upon receipt ofthe sourceblock S, the encoder obtains a codeword C of length L in theusual way, or in conjunction with the permutation shuffling mechanism in(a), then sends (C XOR w_L) 3380. The decoder, upon receiving C′,computes (C′ XOR w_L) (which will equal C), and then decodes it in thestandard way. Again, codebooks can be stored in “XORed” version, butthey must be accompanied by the binary words w_j to use them, or elsethe user must have enough information accompanying the codebook tolocate the w_j for use (perhaps via a separate authenticatedcommunication process). Without having the w_j binary words accompaniedby the encrypted data transmission, this method may effectively andsimply increase entropy of encryption 3390, making it harder forattackers or intruders to compromise the encryption.

FIG. 35 is a flow diagram of an exemplary method 3500 for scanning asequence dataset, according to some embodiments. According to someembodiments, the process begins when a sequence analyzer 3410 receives asequence dataset 3502. The sequence dataset may comprise genomic and/orbioinformatic information, and may be received by sequence analyzer 3410in various file format types. As a next step, a sequence analyzer 3410may analyze the sequence dataset in order to determine what type of fileformat was received by analyzing the binary and underlying codeassociated with the sequence dataset 3504. In some aspects, a databaseof known file types may be present which can be leveraged to compare thereceived sequence dataset file format against the known file types inorder to assist system 3400 in determining the file correct file type.At a next step, 3506 the file type is labeled either as graph-based(e.g., genome graph, directed graph, etc.) or text-based (e.g., wordfile, CSV, FASTA, etc.). If the received sequence dataset is a graphicalrepresentation of genome sequences, then a 3508 graph traversalalgorithm is selected and graph traversal is performed on the graphdata, wherein as the graph is traversed, sequence analyzer 3410 maydetermine the reference node (that is, the node which serves as theorigin of the graph) and assign a first index value to the referencenode and then all subsequent nodes also receive an index value based ontheir position relative to the reference node 3510. As each node on thegraph is traversed and indexed, the sequence data expressed by each nodeis also scanned and a count is maintained of the number of uniquecharacters contained in the graph 3512 and an indication is made atpositions in the graphical dataset where the character count increase bya power of two 3516. These indicated positions may be used by sequenceanalyzer 3410 as locations where sourceblock boundaries may beestablished. At the next step, compaction calculator 3416 may calculateand compare the compaction ratios of using sourceblocks bounded at theindicated positions 3518. The next step 3520 deconstructs the receivedsequence dataset to create a plurality of sourceblocks bound by thepositions that yield the most efficient compaction ratio. As a laststep, 3522 the plurality of sourceblocks can be sent to datadeconstruction engine 3420 where they may be encoded using referencecodes.

If, at step 3506, the file type is text-based, then sequence analyzer3410 may scan through the sequence dataset and maintain a count of theunique characters contained within the received dataset 3514. Thetext-based sequence dataset then proceeds through steps 3516-3522 asdescribed above. It should be appreciated that this diagram is anexemplary flow diagram and does not limit the disclosed system andmethod in any way. In some embodiments, actions and steps of this methodmay be done sequentially as is illustrated in FIG. 35 . In otherembodiments, parallel processing may be leveraged in order to performsome of the steps illustrated in parallel. For example, during graphtraversal processes node indexing and counting unique characters can beperformed in parallel with no loss of performance.

FIGS. 36A-D are exemplary genome graphs that may be processed by system3400 for bandwidth-efficient encoding of genomic data, according to someembodiments. FIGS. 36A-D illustrate four different types of genomegraphs, all constructed from the pair of sequences ATCCCCTA and ATGTCTA.Graphs have a longstanding place in biological sequence analysis, inwhich they have often been used to compactly represent an ensemble ofpossible sequences. FIG. 36A is a representation of a De Bruijin graphwhich is a directed graph representing overlaps between sequences ofsymbols, in this example the symbols are the genetic “letters” of theDNA code, G, C, T, and A, which stand for the various differentchemicals that make up the nucleotide bases of DNA. A De Bruijin graphis comprised of vertices (nodes), consisting of all possible length-nsequences of the given symbols; the same symbol may appear multipletimes in a sequence. If one of the vertices can be expresses as anothervertex by shifting all its symbols by one place to the left and adding anew symbol at the end of the vertex, then the latter has a directed edgeto the former vertex. Each vertex in a De Bruijin graph has the samenumber of input edges and output edges. De Bruijin graphs are populardirected graph representations in which each node represents a k-mer (aunique string of length k), and each directed edge represents an overlapof k−1 bases between the suffix of the “from” node and the prefix of the“to” node. De Bruijin graphs are a restricted class of vertex-labeleddirected graphs. In bioinformatics, De Bruijin graphs are used for denovo assembly of sequencing reads into a genome.

FIG. 36B is a representation of a directed acyclic graph (DAG) that isused to store and convey information about a genome sequence. DAGs maybe the simplest common graph representation in which directed edgesencode a nucleotide sequence. DAGs are graphs whose nodes are labeledsuch that a directed walk can be interpreted as a DNA sequence, definedby the sequence of node labels along the walk. In either edge- orvertex-labeled representations, directed graphs do not fully express theconcept of strand. That is, they do not distinguish between reading aDNA molecule in its forward and reverse complement orientations.

FIG. 36C is a representation of a bidirected graph (also referred to asa sequence graph). To express strandedness, directed graphs can begeneralized to bidirected graphs in which each edge endpoint has anindependent orientation, indicating whether the forward or reversecomplement strand of the attached node is to be visited when enteringthe node through that endpoint of the edge. Inversions, reverse tandemduplications, and arbitrarily complex arrangements are expressible inthe bidirected representation. Such complex variation cannot beexpressed in the directed graph representation without creatingindependent forward and reverse complement nodes and storing additionalinformation to describe information to describe this complementarity.Generally, a bidirected graph in which each node is labeled with anucleotide string is referred to as a sequence graph. In a sequencegraph, a DNA sequence is read out by concatenating the node-orientedlabels of a walk that always enters and exits each node through edgeendpoints with the opposite orientations. Labels are oriented such thatentering through one endpoint orientation encodes the reverse complementof entering the node through the opposite endpoint orientation.

FIG. 36D is a representation of a bi-edged graph (also referred to as abi-edged sequence graph). Bi-edged graphs are the edge-labeled versionof bidirected graphs which give an equivalent representation.

Hardware Architecture

Generally, the techniques disclosed herein may be implemented onhardware or a combination of software and hardware. For example, theymay be implemented in an operating system kernel, in a separate userprocess, in a library package bound into network applications, on aspecially constructed machine, on an application-specific integratedcircuit (ASIC), or on a network interface card.

Software/hardware hybrid implementations of at least some of the aspectsdisclosed herein may be implemented on a programmable network-residentmachine (which should be understood to include intermittently connectednetwork-aware machines) selectively activated or reconfigured by acomputer program stored in memory. Such network devices may havemultiple network interfaces that may be configured or designed toutilize different types of network communication protocols. A generalarchitecture for some of these machines may be described herein in orderto illustrate one or more exemplary means by which a given unit offunctionality may be implemented. According to specific aspects, atleast some of the features or functionalities of the various aspectsdisclosed herein may be implemented on one or more general-purposecomputers associated with one or more networks, such as for example anend-user computer system, a client computer, a network server or otherserver system, a mobile computing device (e.g., tablet computing device,mobile phone, smartphone, laptop, or other appropriate computingdevice), a consumer electronic device, a music player, or any othersuitable electronic device, router, switch, or other suitable device, orany combination thereof. In at least some aspects, at least some of thefeatures or functionalities of the various aspects disclosed herein maybe implemented in one or more virtualized computing environments (e.g.,network computing clouds, virtual machines hosted on one or morephysical computing machines, or other appropriate virtual environments).

Referring now to FIG. 28 , there is shown a block diagram depicting anexemplary computing device 10 suitable for implementing at least aportion of the features or functionalities disclosed herein. Computingdevice 10 may be, for example, any one of the computing machines listedin the previous paragraph, or indeed any other electronic device capableof executing software- or hardware-based instructions according to oneor more programs stored in memory. Computing device 10 may be configuredto communicate with a plurality of other computing devices, such asclients or servers, over communications networks such as a wide areanetwork a metropolitan area network, a local area network, a wirelessnetwork, the Internet, or any other network, using known protocols forsuch communication, whether wireless or wired.

In one aspect, computing device 10 includes one or more centralprocessing units (CPU) 12, one or more interfaces 15, and one or morebusses 14 (such as a peripheral component interconnect (PCI) bus). Whenacting under the control of appropriate software or firmware, CPU 12 maybe responsible for implementing specific functions associated with thefunctions of a specifically configured computing device or machine. Forexample, in at least one aspect, a computing device 10 may be configuredor designed to function as a server system utilizing CPU 12, localmemory 11 and/or remote memory 16, and interface(s) 15. In at least oneaspect, CPU 12 may be caused to perform one or more of the differenttypes of functions and/or operations under the control of softwaremodules or components, which for example, may include an operatingsystem and any appropriate applications software, drivers, and the like.

CPU 12 may include one or more processors 13 such as, for example, aprocessor from one of the Intel, ARM, Qualcomm, and AMD families ofmicroprocessors. In some aspects, processors 13 may include speciallydesigned hardware such as application-specific integrated circuits(ASICs), electrically erasable programmable read-only memories(EEPROMs), field-programmable gate arrays (FPGAs), and so forth, forcontrolling operations of computing device 10. In a particular aspect, alocal memory 11 (such as non-volatile random access memory (RAM) and/orread-only memory (ROM), including for example one or more levels ofcached memory) may also form part of CPU 12. However, there are manydifferent ways in which memory may be coupled to system 10. Memory 11may be used for a variety of purposes such as, for example, cachingand/or storing data, programming instructions, and the like. It shouldbe further appreciated that CPU 12 may be one of a variety ofsystem-on-a-chip (SOC) type hardware that may include additionalhardware such as memory or graphics processing chips, such as a QUALCOMMSNAPDRAGON™ or SAMSUNG EXYNOS™ CPU as are becoming increasingly commonin the art, such as for use in mobile devices or integrated devices.

As used herein, the term “processor” is not limited merely to thoseintegrated circuits referred to in the art as a processor, a mobileprocessor, or a microprocessor, but broadly refers to a microcontroller,a microcomputer, a programmable logic controller, anapplication-specific integrated circuit, and any other programmablecircuit.

In one aspect, interfaces 15 are provided as network interface cards(NICs). Generally, NICs control the sending and receiving of datapackets over a computer network; other types of interfaces 15 may forexample support other peripherals used with computing device 10. Amongthe interfaces that may be provided are Ethernet interfaces, frame relayinterfaces, cable interfaces, DSL interfaces, token ring interfaces,graphics interfaces, and the like. In addition, various types ofinterfaces may be provided such as, for example, universal serial bus(USB), Serial, Ethernet, FIREWIRE™, THUNDERBOLT™, PCI, parallel, radiofrequency (RF), BLUETOOTH™, near-field communications (e.g., usingnear-field magnetics), 802.11 (Wi-Fi), frame relay, TCP/IP, ISDN, fastEthernet interfaces, Gigabit Ethernet interfaces, Serial ATA (SATA) orexternal SATA (ESATA) interfaces, high-definition multimedia interface(HDMI), digital visual interface (DVI), analog or digital audiointerfaces, asynchronous transfer mode (ATM) interfaces, high-speedserial interface (HSSI) interfaces, Point of Sale (POS) interfaces,fiber data distributed interfaces (FDDIs), and the like. Generally, suchinterfaces 15 may include physical ports appropriate for communicationwith appropriate media. In some cases, they may also include anindependent processor (such as a dedicated audio or video processor, asis common in the art for high-fidelity A/V hardware interfaces) and, insome instances, volatile and/or non-volatile memory (e.g., RAM).

Although the system shown in FIG. 28 illustrates one specificarchitecture for a computing device 10 for implementing one or more ofthe aspects described herein, it is by no means the only devicearchitecture on which at least a portion of the features and techniquesdescribed herein may be implemented. For example, architectures havingone or any number of processors 13 may be used, and such processors 13may be present in a single device or distributed among any number ofdevices. In one aspect, a single processor 13 handles communications aswell as routing computations, while in other aspects a separatededicated communications processor may be provided. In various aspects,different types of features or functionalities may be implemented in asystem according to the aspect that includes a client device (such as atablet device or smartphone running client software) and server systems(such as a server system described in more detail below).

Regardless of network device configuration, the system of an aspect mayemploy one or more memories or memory modules (such as, for example,remote memory block 16 and local memory 11) configured to store data,program instructions for the general-purpose network operations, orother information relating to the functionality of the aspects describedherein (or any combinations of the above). Program instructions maycontrol execution of or comprise an operating system and/or one or moreapplications, for example. Memory 16 or memories 11, 16 may also beconfigured to store data structures, configuration data, encryptiondata, historical system operations information, or any other specific orgeneric non-program information described herein.

Because such information and program instructions may be employed toimplement one or more systems or methods described herein, at least somenetwork device aspects may include nontransitory machine-readablestorage media, which, for example, may be configured or designed tostore program instructions, state information, and the like forperforming various operations described herein. Examples of suchnontransitory machine-readable storage media include, but are notlimited to, magnetic media such as hard disks, floppy disks, andmagnetic tape; optical media such as CD-ROM disks; magneto-optical mediasuch as optical disks, and hardware devices that are speciallyconfigured to store and perform program instructions, such as read-onlymemory devices (ROM), flash memory (as is common in mobile devices andintegrated systems), solid state drives (SSD) and “hybrid SSD” storagedrives that may combine physical components of solid state and hard diskdrives in a single hardware device (as are becoming increasingly commonin the art with regard to personal computers), memristor memory, randomaccess memory (RAM), and the like. It should be appreciated that suchstorage means may be integral and non-removable (such as RAM hardwaremodules that may be soldered onto a motherboard or otherwise integratedinto an electronic device), or they may be removable such as swappableflash memory modules (such as “thumb drives” or other removable mediadesigned for rapidly exchanging physical storage devices),“hot-swappable” hard disk drives or solid state drives, removableoptical storage discs, or other such removable media, and that suchintegral and removable storage media may be utilized interchangeably.Examples of program instructions include both object code, such as maybe produced by a compiler, machine code, such as may be produced by anassembler or a linker, byte code, such as may be generated by forexample a JAVA™ compiler and may be executed using a Java virtualmachine or equivalent, or files containing higher level code that may beexecuted by the computer using an interpreter (for example, scriptswritten in Python, Perl, Ruby, Groovy, or any other scripting language).

In some aspects, systems may be implemented on a standalone computingsystem. Referring now to FIG. 29 , there is shown a block diagramdepicting a typical exemplary architecture of one or more aspects orcomponents thereof on a standalone computing system. Computing device 20includes processors 21 that may run software that carry out one or morefunctions or applications of aspects, such as for example a clientapplication 24. Processors 21 may carry out computing instructions undercontrol of an operating system 22 such as, for example, a version ofMICROSOFT WINDOWS™ operating system, APPLE macOS™ or iOS™ operatingsystems, some variety of the Linux operating system, ANDROID™ operatingsystem, or the like. In many cases, one or more shared services 23 maybe operable in system 20, and may be useful for providing commonservices to client applications 24. Services 23 may for example beWINDOWS™ services, user-space common services in a Linux environment, orany other type of common service architecture used with operating system21. Input devices 28 may be of any type suitable for receiving userinput, including for example a keyboard, touchscreen, microphone (forexample, for voice input), mouse, touchpad, trackball, or anycombination thereof. Output devices 27 may be of any type suitable forproviding output to one or more users, whether remote or local to system20, and may include for example one or more screens for visual output,speakers, printers, or any combination thereof. Memory 25 may berandom-access memory having any structure and architecture known in theart, for use by processors 21, for example to run software. Storagedevices 26 may be any magnetic, optical, mechanical, memristor, orelectrical storage device for storage of data in digital form (such asthose described above, referring to FIG. 28 ). Examples of storagedevices 26 include flash memory, magnetic hard drive, CD-ROM, and/or thelike.

In some aspects, systems may be implemented on a distributed computingnetwork, such as one having any number of clients and/or servers.Referring now to FIG. 30 , there is shown a block diagram depicting anexemplary architecture 30 for implementing at least a portion of asystem according to one aspect on a distributed computing network.According to the aspect, any number of clients 33 may be provided. Eachclient 33 may run software for implementing client-side portions of asystem; clients may comprise a system 20 such as that illustrated inFIG. 29 . In addition, any number of servers 32 may be provided forhandling requests received from one or more clients 33. Clients 33 andservers 32 may communicate with one another via one or more electronicnetworks 31, which may be in various aspects any of the Internet, a widearea network, a mobile telephony network (such as CDMA or GSM cellularnetworks), a wireless network (such as Wi-Fi, WiMAX, LTE, and so forth),or a local area network (or indeed any network topology known in theart; the aspect does not prefer any one network topology over anyother). Networks 31 may be implemented using any known networkprotocols, including for example wired and/or wireless protocols.

In addition, in some aspects, servers 32 may call external services 37when needed to obtain additional information, or to refer to additionaldata concerning a particular call. Communications with external services37 may take place, for example, via one or more networks 31. In variousaspects, external services 37 may comprise web-enabled services orfunctionality related to or installed on the hardware device itself. Forexample, in one aspect where client applications 24 are implemented on asmartphone or other electronic device, client applications 24 may obtaininformation stored in a server system 32 in the cloud or on an externalservice 37 deployed on one or more of a particular enterprise's oruser's premises.

In some aspects, clients 33 or servers 32 (or both) may make use of oneor more specialized services or appliances that may be deployed locallyor remotely across one or more networks 31. For example, one or moredatabases 34 may be used or referred to by one or more aspects. Itshould be understood by one having ordinary skill in the art thatdatabases 34 may be arranged in a wide variety of architectures andusing a wide variety of data access and manipulation means. For example,in various aspects one or more databases 34 may comprise a relationaldatabase system using a structured query language (SQL), while othersmay comprise an alternative data storage technology such as thosereferred to in the art as “NoSQL” (for example, HADOOP CASSANDRA™,GOOGLE BIGTABLE™, and so forth). In some aspects, variant databasearchitectures such as column-oriented databases, in-memory databases,clustered databases, distributed databases, or even flat file datarepositories may be used according to the aspect. It will be appreciatedby one having ordinary skill in the art that any combination of known orfuture database technologies may be used as appropriate, unless aspecific database technology or a specific arrangement of components isspecified for a particular aspect described herein. Moreover, it shouldbe appreciated that the term “database” as used herein may refer to aphysical database machine, a cluster of machines acting as a singledatabase system, or a logical database within an overall databasemanagement system. Unless a specific meaning is specified for a givenuse of the term “database”, it should be construed to mean any of thesesenses of the word, all of which are understood as a plain meaning ofthe term “database” by those having ordinary skill in the art.

Similarly, some aspects may make use of one or more security systems 36and configuration systems 35. Security and configuration management arecommon information technology (IT) and web functions, and some amount ofeach are generally associated with any IT or web systems. It should beunderstood by one having ordinary skill in the art that anyconfiguration or security subsystems known in the art now or in thefuture may be used in conjunction with aspects without limitation,unless a specific security 36 or configuration system 35 or approach isspecifically required by the description of any specific aspect.

FIG. 31 shows an exemplary overview of a computer system 40 as may beused in any of the various locations throughout the system. It isexemplary of any computer that may execute code to process data. Variousmodifications and changes may be made to computer system 40 withoutdeparting from the broader scope of the system and method disclosedherein. Central processor unit (CPU) 41 is connected to bus 42, to whichbus is also connected memory 43, nonvolatile memory 44, display 47,input/output (I/O) unit 48, and network interface card (NIC) 53. I/Ounit 48 may, typically, be connected to peripherals such as a keyboard49, pointing device 50, hard disk 52, real-time clock 51, a camera 57,and other peripheral devices. NIC 53 connects to network 54, which maybe the Internet or a local network, which local network may or may nothave connections to the Internet. The system may be connected to othercomputing devices through the network via a router 55, wireless localarea network 56, or any other network connection. Also shown as part ofsystem 40 is power supply unit 45 connected, in this example, to a mainalternating current (AC) supply 46. Not shown are batteries that couldbe present, and many other devices and modifications that are well knownbut are not applicable to the specific novel functions of the currentsystem and method disclosed herein. It should be appreciated that someor all components illustrated may be combined, such as in variousintegrated applications, for example Qualcomm or Samsungsystem-on-a-chip (SOC) devices, or whenever it may be appropriate tocombine multiple capabilities or functions into a single hardware device(for instance, in mobile devices such as smartphones, video gameconsoles, in-vehicle computer systems such as navigation or multimediasystems in automobiles, or other integrated hardware devices).

In various aspects, functionality for implementing systems or methods ofvarious aspects may be distributed among any number of client and/orserver components. For example, various software modules may beimplemented for performing various functions in connection with thesystem of any particular aspect, and such modules may be variouslyimplemented to run on server and/or client components.

The skilled person will be aware of a range of possible modifications ofthe various aspects described above. Accordingly, the present inventionis defined by the claims and their equivalents.

What is claimed is:
 1. A system for bandwidth-efficient encoding of genomic data, comprising: a computing device comprising a processor, and a memory; a codebook stored in the memory of the computing device, the codebook comprising a plurality of sourceblocks and, for each sourceblock, a reference code; a sequence analyzer comprising a first plurality of programming instructions stored in the memory and operable on the processor, wherein the first plurality of programming instructions, when operating on the processor, cause the processor to: receive a sequence dataset; scan the sequence dataset and maintain a count of the number of unique characters contained within the sequence dataset; for each occurrence of a unique character which causes the count of the number of unique characters to reach a value equal to a power of two: indicate a position in the sequence dataset corresponding to the unique character; calculate a compaction ratio that would be obtained by dividing the sequence dataset into one of a plurality of segments at one of the indicated positions; deconstruct the sequence dataset into a plurality of deconstructed sourceblocks at the positions that yield the best compaction ratio; and pass the plurality of deconstructed sourceblocks to a data deconstruction engine; and a data deconstruction engine comprising a second plurality of programming instructions stored in the memory and operable on the processor, wherein the second plurality of programming instructions, when operating on the processor, cause the processor to: receive the plurality of deconstructed sourceblocks from the sequence analyzer; pass the plurality of deconstructed sourceblocks to a library management module for comparison with sourceblocks already contained in the codebook; receive a reference code for each of the deconstructed sourceblocks from the library management module; and create a plurality of codewords for storage or transmission of the sequence dataset; and a library management module comprising a third plurality of programming instructions stored in the memory and operable on the processor, wherein the third plurality of programming instructions, when operating on the processor, cause the processor to: receive the plurality of deconstructed sourceblocks from the data deconstruction engine; for each of the plurality of deconstructed sourceblocks, return a reference code to the data deconstruction engine, when the respective received deconstructed sourceblock matches an existing sourceblock in the codebook; and for each received deconstructed sourceblock that is not present in the codebook: create a new, unique reference code for the respective deconstructed sourceblock; store both the respective deconstructed sourceblock and the associated reference code in the codebook; and return the new reference code to the data deconstruction engine.
 2. The system of claim 1, wherein each of the plurality of codewords contains at least a reference code to a sourceblock in the codebook, and may contain additional information about the location of the reference code within the sequence dataset.
 3. The system of claim 1, wherein the sequence dataset that is encoded comprises a graph representing at least a portion of a plurality of genomes.
 4. The system of claim 3, wherein the graph is a De Bruijin graph, a directed graph, a bi-edged graph, or a bidirected graph.
 5. The system of claim 1, further comprising a data reconstruction engine comprising a fourth plurality of programming instructions stored in the memory and operable on the processor, wherein the fourth plurality of programming instructions, when operating on the processor, cause the processor to: receive a request for a reconstructed sequence dataset; retrieve the codewords associated with the requested data from the codebook; pass the reference codes contained in the codewords to the library management module for retrieval of the sourceblock contained in the codebook associated with the reference codes; assemble the retrieved sourceblocks in proper order based on location information contained in the codewords; and send out the reconstructed sequence dataset to the requester.
 6. A method for bandwidth-efficient encoding of genomic data, comprising the steps of: storing, in a memory of a computing device, a codebook, the codebook comprising a plurality of sourceblocks and for each sourceblock a reference code; receiving a sequence dataset; scanning the sequence dataset and maintaining a count of the number of unique characters contained within the sequence dataset; for each occurrence of a unique character which causes the count of the number of unique characters to reach a value equal to a power of two: indicating a position in the sequence dataset corresponding to the unique character; calculating a compaction ratio that would be obtained by dividing the sequence dataset into one of a plurality of segments at one of the indicated positions; deconstructing the sequence dataset into a plurality of deconstructed sourceblocks at the positions that yield the best compaction ratio; passing the plurality of deconstructed sourceblocks to a data deconstruction engine; receiving the plurality of deconstructed sourceblocks from the sequence analyzer; passing the plurality of deconstructed sourceblocks to a library management module for comparison with sourceblocks already contained in the codebook; receiving a reference code for each of the deconstructed sourceblocks from the library management; creating a plurality of codewords for storage or transmission of the sequence dataset; receiving the plurality of deconstructed sourceblocks from the data deconstruction engine; for each of the plurality of deconstructed sourceblocks, returning a reference code to the data deconstruction engine, when the respective received deconstructed sourceblock matches an existing sourceblock in the codebook; and for each received deconstructed sourceblock that is not present in the codebook: creating a new, unique reference code for the respective deconstructed sourceblock; storing both the respective deconstructed sourceblock and the associated reference code in the codebook; and returning the new reference code to the data deconstruction engine.
 7. The method of claim 6, wherein each of the plurality of codewords contains at least a reference code to a sourceblock in the codebook, and may contain additional information about the location of the reference code within the sequence dataset.
 8. The method of claim 6, wherein the sequence dataset that is encoded comprises a graph representing at least a portion of a plurality of genomes.
 9. The method of claim 8, wherein the graph is a De Bruijin graph, a directed graph, a bi-edged graph, or a bidirected graph.
 10. The method of claim 6, further comprising the steps of: receiving a request for a reconstructed sequence dataset; retrieving the codewords associated with the requested data from the codebook; passing the reference codes contained in the codewords to the library management module for retrieval of the sourceblock contained in the codebook associated with the reference codes; assembling the sourceblock in the proper order based on the location information contained in the codewords; and sending out the reconstructed sequence dataset to the requester. 